Addressing Provider Stress and Unconscious Bias to Improve Quality of Maternal Health Care
Phase: NA
Conditions: Maternal Health, Mental Health, Healthcare Provider, Adverse Outcomes, Stress, Stress, Psychological, Stress, Emotional, Quality of Care, Burnout, Discrimination, Social
Sponsor: University of California, San Francisco

 
Version 3.0: Sep 28, 2020 
 
 
Title of the project:  
Addressing Provider Stress and Unconscious Bias to Improve Quality of Maternal 
Health Care: Caring for Providers to Improve Patient experience (CPIPE) study 
phase 2 in Migori County 
 
ClinicalTrials.gov Identifier: NCT05019131  
 
Investigators and institutional affiliations:  
 
Name and Qualifications 
Position 
Institutional Affiliation 
Patience Afulani, MBChB, 
MPH, PhD 
Principal Investigator 
UCSF 
Linnet Ongeri, MBBS, MMed  Co-PI 
KEMRI 
Joyceline Kinyua, MPH 
Co-Investigator 
KEMRI 
 
This project is part of an NIH transition to independence (K99/R00) award. CPIPE1 was 
the K99 phase and CPIPE2 (this project) will be the R00 phase 
 
 
 
Version 3.0  Sep 28, 2020

 
Version 3.0: Sep 28, 2020 
 
Contents 
1 
Title of the project ....................................................................................................................... 1 
2 
Investigators and institutional affiliations: .................................................................................. 1 
3 
Table of Content ......................................................................... Error! Bookmark not defined. 
4 
Abstract ....................................................................................................................................... 3 
5 
Lay summary ............................................................................................................................... 3 
6 
Introduction/Background ............................................................................................................ 3 
7 
Justification for the study ............................................................................................................ 5 
8 
General Objective:....................................................................................................................... 6 
9 
METHODOLOGY: ..................................................................................................................... 7 
10 
(a) Study site (Geographical) ...................................................................................................... 7 
11 
(b) Study design .......................................................................................................................... 7 
12 
(c) Study populations .................................................................................................................. 8 
13 
(d)Sampling ................................................................................................................................. 8 
14 
(e) Procedures .............................................................................................................................. 9 
15 
Data management ...................................................................................................................... 12 
16 
(b) Data storage ......................................................................................................................... 12 
17 
(c)Data management and analysis plan ..................................................................................... 13 
18 
Ethical considerations ............................................................................................................... 13 
19 
Expected application of the results ............................................................................................ 14 
20 
Time frame/duration of the project ........................................................................................... 14 
21 
Role of Investigators ................................................................................................................. 15 
22 
References ................................................................................................................................. 16 
23 
 
24 
 
25 
 
26 
 
27 
 
28 
 
29 
 
30 
 
31 
 
32 

  
Page 3 of 28 
Abstract  
 
Poor person-centered maternal health care (PCMHC) contributes to high maternal and neonatal mortality in 
sub-Saharan Africa (SSA), and disparities in PCMHC are driving disparities in use of maternal health services., 
However, little research exists on how to improve PCMHC and reduce disparities. We seek to fill this gap with 
this project. We propose targeting health provider stress and unconscious bias as fundamental factors driving 
both poor PCMHC and disparities in PCMHC. Health care provider stress and unconscious bias are important 
to consider because: (1) providers in low-resource settings often work under very stressful conditions; (2) 
unconscious bias is prevalent in every society including SSA; and (3) these factors are mutually reinforcing 
drivers of poor quality care and disparities in person-centered care. In the first phase of the project (CPIPE1), 
we conducted research to examine (1) the factors associated with PCMHC and identified provider stress and 
unconscious bias as key contributing factors. We also examined the levels of provider stress and unconscious 
bias and the types of stressors and biases in Migori County, Kenya. The results of that research will be used to 
inform this phase (CPIPE2), the aims of which are to: (1) design a multicomponent theory and evidence-based 
intervention that enables providers to identify and manage their stress and unconscious bias; (2) pilot the 
intervention to assess its feasibility and acceptability; and (3) assess preliminary effect of the intervention on: 
(a) provider knowledge, attitudes, and behaviors related to stress and unconscious bias; and (b) provider stress 
levels using a pretest-posttest control group design. We will use the results of the pilot to refine the intervention 
and develop an R01 proposal for a multi-site evaluation with a larger sample and longer follow up to assess 
impact on PCMHC. This study will yield valuable information to inform quality improvement efforts for 
PCMHC. 
 
 
Lay summary 
 
The activities described in this proposal are aimed at addressing health care provider stress and unconscious 
bias to improve quality of maternal health care, particularly related to the person-centered dimensions of care—
i.e. care that is respectful and responsive to women’s needs, preferences, and values. We focus on health 
provider stress and unconscious bias because they are key drivers of poor-quality care that are often not 
addressed in interventions designed to improve quality of maternal health care. We plan to (1) design an 
intervention that enables providers to identify and manage their stress and unconscious bias; (2) pilot the 
intervention to assess its feasibility and acceptability; and (3) assess preliminary effect of the intervention on: 
(a) provider knowledge, attitudes, and behaviors related to stress and unconscious bias; and (b) provider stress 
levels.  
 
 
 
 
 
 
Introduction/Background 
 
Of the estimated 800 pregnancy-related deaths that occur daily, about 99% occur in low- and middle-income 
countries (LMICs), with about two-thirds occurring in sub-Saharan Africa (SSA) alone.1 Skilled care in health 
facilities is critical to reducing maternal mortality, as well as to improving neonatal outcomes. Yet, only about 
half of births in SSA occur in health facilities—with wide disparities, especially by socioeconomic status 
(SES).2,3 For example, only 31% of women in the lowest wealth quintile in Kenya deliver in a health facility 

  
Page 4 of 28 
compared to 93% among those in the highest wealth quintile. Poor Person-Centered Maternal Health Care 
(PCMHC) is a key factor driving both the low coverage for facility deliveries and high maternal mortality.4–8 
PCMHC refers to care that is respectful and responsive to individual women and their families’ preferences, 
needs, and values.9 PCMHC is an emerging concept in LMICs motivated by several studies in the last few years 
that have documented disrespectful, abusive, and neglectful treatment of women in facilities during 
childbirth.10–13 Poor PCMHC has multiplicative effects, as it can directly lead to poor pregnancy outcomes, as 
well as decrease demand for services.14,8 Yet, little research exists on effective interventions to improve 
PCMHC in low-resource settings like in SSA.15,16 
 
High maternal mortality and morbidity in SSA: Maternal mortality and morbidity remains very high in SSA, 
despite progress in the last decade.1,25 The estimated maternal mortality ratio for SSA is at a high 546 per 
100,000 live births—with that of Kenya at 510—compared to about 12 per 100,000 live births in high-income 
regions.25 For every woman who dies, about 20 others suffer from various disabilities.26 These poor maternal 
outcomes go hand in hand with poor fetal outcomes including stillbirths, prematurity, and early neonatal 
deaths.7 Various factors across the life course account for the poor MCH outcomes in SSA, hence a need for 
interventions across the life course. Skilled attendance at delivery is, however, critical to preventing maternal 
and neonatal mortality, as about three-quarters of maternal and fetal deaths occur from complications during 
labor, delivery, and the first 24 hours postpartum.27 These complications are difficult to predict, but can be 
effectively managed and deaths averted if they are recognized and treated promptly.27–29 Thus, there is the need 
for a skilled birth attendant (SBA)—a health professional who can identify and manage normal labor and 
delivery and who can identify and treat complications or provide basic care and referral—at every delivery.27,28 
In most of SSA, deliveries by SBAs only occur in health facilities.2,3 
 
Poor coverage and quality of maternal health services contributing to poor outcomes: While many 
countries report increasing coverage for facility births, significant gaps in coverage, equity, and quality of care 
persists between and within countries.30,31 For example, in Kenya, less than a third (31%) of women in the 
lowest wealth quintile give birth in a health facility, compared to almost all (93%) among those in the highest 
wealth quintile.32 Until recently, many efforts in SSA to improve MCH outcomes focused on increasing 
coverage for use of MCH services. Documentation of poor quality care in many health facilities and evidence 
that increasing facility deliveries have not paralleled reductions in mortality have heightened attention on 
quality of care.4,5,7,14,33 But efforts in SSA have focused on the technical aspects of quality of care. Fewer 
studies have addressed the person-centered dimensions. 
 
The role of person-centered maternal health care: PCMHC refers to maternal health care that is respectful of 
and responsive to women and their families’ preferences, needs, and values.9 PCMHC thus includes system and 
provider responsiveness, patient-provider communication, interpersonal treatment, and related constructs.14,8 
Disrespectful, abusive, and neglectful treatment of women in facilities during childbirth reflect very poor 
PCMHC. Such mistreatment deters women from giving birth in health facilities.10–13 The experience of poor 
PCMHC, even by a few women, leads to poor community perceptions of quality of care, which discourages 
many other women from delivering in health facilities.6,34 Disparities in PCMHC are in particular driving the 
disparities in facility deliveries in SSA, as women of low SES, adolescents, minority tribes, and those with HIV 
are more likely to be mistreated and stigmatized in health facilities.2,3,6,35 PCMHC also has direct effects on 
maternal and neonatal outcomes through pathways such as better clinical decision-making, improved provider-
patient communication, increased adherence to treatments, and improved psychosocial health.14,8,36,37 Poor 
PCMHC, therefore, leads to delayed, inadequate, unnecessary, or harmful care, minimizing health gains for 
both mothers and babies.14,8 
 
This project seeks to extend knowledge in this area by examining two factors—provider stress and unconscious 
bias (UB)—that are driving poor PCMHC and contributing to disparities in PCMHC. Addressing provider 
stress and UB has not yet received attention in the quality of care dialogue in SSA, although it is well- 

  
Page 5 of 28 
recognized that providers in SSA work under very stressful conditions and UB is prevalent in every society.17,18 
Our initial focus on these factors stemmed from the theory that disrespectful behavior towards patients results 
from characteristics of providers and their responses to stressful environments. Additionally, disrespect thrives 
in a culture that tolerates disrespect and individual biases may reinforce patterns of abuse.19,20 Thus, in 
hierarchical societies where people of low status are more likely to be disrespected,21 providers may be 
unconsciously mistreating women of low status. Our research in the first phase of the project provided support 
for this theory.57,82 In this phase, we hypothesize that a multicomponent intervention that enables providers to 
identify and manage their stress and to be conscious of their biases and how their actions affect women’s 
experiences will improve PCMHC and reduce disparities in PCMHC (Figure 2). The knowledge, skills, and the 
evidence from the first phase, will be used to develop this intervention. Addressing stress and UB together 
acknowledges both the vulnerability and the power of providers, and highlights outcomes not just for the 
patient, but also for the provider.  
 
Justification for the study 
Despite increasing recognition of the importance of PCMHC, little empirical research exists on effective 
interventions to address it in low-resource settings.17 Two recent systematic reviews on interventions to improve 
PCMHC yielded only a handful of papers addressing some dimensions of PCMHC in SSA.38,39 These 
interventions focus on educating women to advocate for themselves, labor support, and training providers on 
respectful care, and to improve patient-provider communication.15,40 One intervention also involved working 
with policymakers to encourage greater focus on disrespect and abuse of women and strengthening linkages 
between the facility and community for accountability and governance.40 These efforts, while important, fail to 
address some of the fundamental factors that may be driving poor PCMHC, particularly among poor and other 
marginalized groups in health facilities. This project seeks to help bridge this gap by examining two interrelated 
but under explored factors contributing to poor PCMHC and to disparities in PCMHC. 
 
There are currently no evidence-based interventions to improve PCMHC in SSA that address health provider 
stress and unconscious bias (UB). While it is recognized that providers in SSA work under very stressful 
conditions, provider stress is hardly considered in interventions to improve quality of care in SSA.17,41,42 This is 
even more so the case for UB, which is hardly acknowledged in this setting, although it is a prominent topic in 
developed settings like the US.43–47  
 
Stress is the psychological and physiological response to environmental stressors.48 Prolonged stress without 
adequate coping mechanisms leads to burnout, which manifests as overwhelming exhaustion, feelings of 
cynicism, detachment from the job, lower productivity and effectiveness, decreased job satisfaction, and 
reduced commitment to the job.49,50 This leads to poor quality of care with risks to patient safety and poor 
attitudes towards patients.51–53 Provider stress and burnout is also associated with poor health outcomes such as 
depression, cardiovascular disease, and premature mortality, and is expensive for the health system as it leads to 
absenteeism and high staff turnover.49,51,54 Addressing provider stress is therefore important from a broader 
public health perspective, beyond improving PCMHC.49 The potential stressors providers in SSA have to deal 
with are numerous: including feelings of inadequacy in the face of high maternal and newborn mortality; an 
overwhelming work load from staff shortages; not being able to provide best practice due to lack of drugs, 
supplies, and equipment; being required to manage complications beyond their competency; financial strain 
from poor remuneration; poor working conditions with insufficient basic resources, including scarcity of water 
and sanitation; and disrespectful and violent behavior from patients.17,55,56 Disrespectful behavior towards 
patients is a response to these stressful work conditions.57 Yet, few of providers have had training on how to 
cope or deal with stress—only about 10% of the providers we interviewed in Migori, Kenya had received any 
form of training on how to reduce stress.  
 

  
Page 6 of 28 
Bias is the negative evaluation of one group relative to another. It can be explicit/conscious or implicit/ 
unconscious.45 UB operates at an unintentional level and so does not require a person to endorse it or devote 
attention to its expression. Instead, it can be activated quickly and unknowingly by situational cues such as a 
person’s skin color, accent, or low status clothes.45,58 UB is prevalent in every society, although the type of 
biases may differ in different contexts.18,44 In the US, where racial bias has been extensively studied, greater 
pro-White bias among physicians is associated with disparities in quality of care, with lower likelihood of 
prescribing thrombolytics for Black compared to White patients with acute coronary syndrome;59 lower 
inclination to prescribe pain medications for Black versus White children;60 and poorer ratings of interpersonal 
care among Black versus White patients.43 Our work in Kenya supports the critical role of UB in PCMHC 
disparities by SES, age, tribe, and HIV status.57  
 
The way people are treated in healthcare settings is a reflection of the broader societal norms and behaviors.17 In 
societies where gender-based violence, disrespect of the poor, tribalism, and differential treatment based on SES 
are normative—and providers are higher in the social hierarchy than patients—providers are more likely to 
(unconsciously) treat patients with disrespect.17,21 Providers are, however, more likely to be conscious of their 
actions and to treat patients with respect when they meet someone who challenges their social standing. Low 
SES women hardly challenge how they are treated, reinforcing mistreatment towards them. This supports the 
idea that disrespect thrives in a culture that tolerates disrespect, and individual biases may reinforce patterns of 
abuse.19,20 Addressing biases against marginalized groups is especially important in Migori County given the 
poor status of women, the high rates of teenage pregnancy, and the high prevalence of HIV in the area.32,61,62   
 
Research suggests that deeply felt biases are more likely to emerge when people are stressed.58 In addition, 
interventions such as mindfulness practices have been shown to have direct effects on implicit bias as well as 
indirect effects through stress reduction.63 Furthermore, addressing provider stress and UB together 
acknowledges both the vulnerability and power of providers, and highlights outcomes not just for the patient, 
but also for the provider. As a result, providers are less likely to feel blamed and more likely to support such an 
intervention. This research is timely, given the increasing evidence of poor PCMHC in SSA, but a lack of 
evidence-based interventions to improve it. It is also innovative as it addresses factors rarely considered in 
MCH quality improvement efforts in SSA and bridges disciplines that have traditionally worked independently. 
The focus on stress is particularly relevant in the current pandemic given the increasing stress providers are 
experiencing. The proposed intervention will be among the first, if not the first, rigorously designed and 
evaluated intervention to improve PCMHC that addresses provider stress and UB as underlying factors.  
 
General Objective: 
To develop and test a locally delivered intervention for addressing provider stress and unconscious bias among 
maternal health providers in Migori, Kenya.  
 
The specific objectives are: 
To design a multicomponent theory and evidence-based intervention that enables providers to identify and 
manage their stress and unconscious bias in Migori County 
To pilot the intervention developed in Aim 1 to assess its feasibility and acceptability 
To assess preliminary effect of the intervention on: (a) Providers’ knowledge, attitudes, and behaviors related to 
stress and unconscious bias; and (b) Provider stress levels.  
 
 

  
Page 7 of 28 
METHODOLOGY:  
(a) Study site (Geographical)  
The intervention will be implemented in the county hospital and one sub-county hospital in Migori County. The 
sub-county hospital will be selected together with the advisory board after the initial formative work. Migori 
County is the primary site of the Preterm Birth Initiative (PTBi)-Kenya and the site of our current project 
CPIPE (SERU 3682). It has eight sub-counties, each of which has a sub-county hospital. There is also one 
county referral hospital and several health centers and faith-based and private health facilities. There are 32 
nurses, 19 clinical officers, and four doctors per 100,000 people in the county. The county population is 
approximately one million, with an estimated 40,000 births annually. The estimated maternal mortality ratio is 
high at 673 deaths per 100,000 live births compared to 495/100,000 nationally. Based on the most recent Kenya 
demographic and health survey, 53% of births in the county occurred in health facilities, compared to the 
national average of 61%. 
 
(b) Study design  
AIM 1: Design a multicomponent theory and evidence-based intervention that enables providers to identify and 
manage their stress and unconscious bias. The intervention design will be an iterative process informed by 
formative research and continuous feedback in consultation with key stakeholders and experts. About 20 
stakeholders including county and sub-county health officials, facility and unit heads, and different cadres 
health care providers will be interviewed. Qualitative research will inform the adaptation of the intervention 
using implementation science methodology. The intervention will be informed by the Social Cognitive 
Theory,22 Trauma Informed System framework,23 and the Ecological Perspective.24 It will include provider 
training using adult learning concepts and simulation to engage providers, increase self-efficacy, and stimulate 
behavior change; as well as foster leadership engagement, and use of embedded champions to create an 
enabling environment for individual behavior change. The curriculum will be short to be taught in less than a 
day (to avoid taking providers away from their work for prolonged periods), but with subsequent booster 
sessions. It will be easy to understand and apply so that it can be taught to all cadres of health providers and 
disseminated using a training of trainers’ approach.  
 
AIM 2: Pilot the intervention developed in Aim 1 to assess its feasibility and acceptability: We will pilot the 
intervention with about 40 health workers (doctors, clinical officers, nurses, and midwives) in Migori County, 
Kenya. In the formative phase, we will assess the feasibility and acceptability of including maternity 
support staff such as ward aids. We will collect data on process measures and document all steps of the 
implementation process to assess feasibility and acceptability of various components of the intervention, and to 
assess intervention fidelity. We will use quantitative and qualitative methods to evaluate the implementation 
process.  
 
AIM 3: Assess preliminary effect of the intervention on: (a) Providers’ knowledge, attitudes, and behaviors 
related to stress and unconscious bias; and (b) Provider stress levels. We will use a pretest-posttest control 
group design with quantitative measures of  

  
Page 8 of 28 
 
provider 
knowledge, 
attitudes, 
and 
behaviors 
related 
to 
stress 
and 
unconscious 
bias 
to 
assess 
the 
preliminary 
effect of the 
intervention 
on these intermediate outcomes (Figure 1). We will also assess preliminary effectiveness on provider stress 
levels using validated psychological measures of stress and biomarkers.  
 
(c) Study populations  
The main study populations are health care providers in the health facilities in Migori County 
 
Criteria for inclusion of subjects  
Providers working in maternity units of the intervention facilities 
Willing and able to provide informed consent 
 
Criteria for exclusion of subjects  
Inability to attend scheduled training. 
 
(d)Sampling  
(i) Sample size determination  
For the qualitative formative research, a sample size of 20 is estimated to achieve saturation.  For the 
evaluation, we expect to enroll 80 providers, with 40 in the intervention group and 40 in control group. This is a 
pilot study and the sample size of 80 is based on feasibility: on an assumption that about 50% of providers in 
the selected sub-counties are likely to participate in the intervention. This sample size is powered (at 80%) to 
detect an effect size of about 0.5. The results will be used to determine the sample size for the R01 clinical trial 
 
(ii) Sampling procedure  
About 20 people, including providers and county leadership, will be recruited for the formative research which 
will use in-depth interviews and 80 providers for the evaluation, which will use both structured and in-depth 
interviews. 40 providers will be assigned to the intervention group and another 40 in the control group. 
Assignment will be at the facility level, based on which facilities are selected as the intervention and control 
facilities. Thus, all providers in the two intervention facilities will be assigned to the intervention group. 
Providers from other facilities will be assigned to the control group. All 80 providers will be asked to participate 
in the baseline and end line surveys. We will collect contact information at baseline to follow up for the end line 
survey for the evaluation data. Providers will be purposively selected based on their position in the facility and 
involvement in maternity care. Providers will include both male and female clinical and support staff such as 
ward aids and community health workers who assist in various roles on the ward. We will assess the feasibility 
and acceptability of including the support staff in the formative phase. 
 
Create enabling environment 
Provider support groups 
and mentoring; engaging 
leadership; embedded 
champions; continuous 
training & evaluation
Increase self-efficacy & attitudes 
towards adopting positive coping 
mechanisms & avoiding bias
Reciprocal 
determinism; 
Observational 
learning & 
Reinforcemen
Goals 
Outcome 
expectancies 
Train providers on  
stress management, UB, 
& PCMHC using 
simulation & adult
Increase knowledge about 
stress, UB, & PCMHC  
Theoretical 
constructs 
Intervention strategies 
Intermediate outcomes (R00) 
Distal 
outcomes 
Figure 1: Conceptual Model 
Change behavior to adopt positive 
coping mechanisms & address 
Decrease 
maternal & 
neonatal 
mortality & 
morbidity
Self-efficacy & 
behavioral 
capability 
Communicate benefits of 
intervention to providers  
Providers set personal 
incremental change 
Higher provider engagement 
Increase 
PCMHC & 
general quality 
of care;
Increase use 
of services & 
reduced 
disparities
Increase resilience & decrease stress & 
bi

  
Page 9 of 28 
(e) Procedures  
AIM 1 (Year 1): Design a multicomponent theory and evidence-based intervention that enables providers 
to identify and manage their stress and unconscious bias. We will develop a theory and evidence-based 
intervention that is relevant and acceptable in Kenya and can be easily implemented and scaled up. The target 
population for this project is health workers in maternity units as well as other support units in Migori County—
the primary site of the Preterm Birth Initiative (PTBi)-Kenya and the site of my current project. We intend to 
build off the PTBi infrastructure including existing relationships for the next phase of the research. Given that 
the sources of stress are difficult to avoid in the life of a provider in a low resource setting, our approach focuses 
on the factors that influence the stress response—which is providers’ perceptions of the stressors, coping 
strategies, and their general wellbeing.48 For UB, a first step towards creating structures to minimize it is 
recognizing it.18 Additionally, people have to be concerned about the effects of UB, to be motivated to learn 
when biased responses are likely to occur, and to learn how to replace biased response with responses more 
consistent with their goals.64  
 
In CPIPE 1, we reviewed existing interventions to address stress and UB and identified some that could be 
adapted for Kenya. The results of the previous CPIPE1 will serve as the initial formative research for the 
intervention. Additionally, at the beginning of this study, we will conduct in-depth-interviews with key 
stakeholders including providers, hospital heads, and the county health director, as well as with experts on stress 
and UB, to help select and refine the intervention. This process will involve presenting potentially useful 
interventions identified from the literature to stakeholders and asking them to assess the interventions in terms 
of feasibility, relevance, and potential for impact, and to suggest changes to improve the intervention.  
 
We will establish a local advisory board to inform this process, with a goal of developing a sense of local 
ownership—a critical component to the sustainability of the project. The formative research and intervention 
development will be an iterative process. Initial interviews will yield preliminary data to inform the type of 
intervention and subsequent interviews will help refine and improve the quality and acceptability of the 
intervention. Consistent with qualitative methods, we will use an evolving semi-structured guide, whereby 
information from prior interviews will inform future interviews. Interviews will be recorded, transcribed, and 
analyzed using thematic analysis.65 
 
The formative work using qualitative methods within an implementation science framework will inform the 
final intervention. We will use the PRECEDE PROCEED implementation science framework, which guides the 
identification of predisposing, enabling, and reinforcing factors that facilitate the success of a health 
intervention. The intervention will also be informed by the Social Cognitive Theory (SCT),22 Trauma Informed 
Systems (TIS) framework,23 and the Ecological Perspective.24 SCT describes a dynamic process in which 
personal factors, environmental factors, and human behavior exert influence upon each other (reciprocal 
determinism). SCT posits that, if individuals have a sense of self-efficacy, they can change their behavior even 
when faced with obstacles. Also, people are more likely to change if they believe the activity has benefits to 
them (outcome expectancy), and if there are tangible goals, positive role models (observational learning), and 
reinforcement.22 These SCT constructs will inform the intervention strategies (Figure 1). For example, to 
decrease resistance and increase their engagement, the intervention will emphasize benefits of stress reduction 
and coping strategies for the provider, as well as for women and their babies. The training will also use 
simulation, a technique of choice in training professional teams, to evoke real life scenarios in an interactive 
fashion to increase self-efficacy,66 and adult learning 
techniques such as 
self-directed inquiry.67  
 
 The TIS framework recognizes stress as a source of 
trauma to the system, 
which if not addressed leads to numbing, reactivity, 
and 
depersonalization.23,68 TIS supports “reflection in 
place 
of 
reaction, 
curiosity in lieu of numbing, self-care instead of 
self-sacrifice, 
and 
Policy level: 
County Health leadership 
 
Interpersonal level: 
Support groups; mentorship 
Community level: 
Embedded champions 
Institutional level: 
Facility leadership  
Figure 2: Ecological approach 
Intrapersonal level:  
Provider training 
 

  
Page 10 of 28 
collective impact rather than siloed structures.”23 Potential TIS curriculum content is included in Table 1. The 
curriculum will also include content on UB, PCMHC, mindfulness, quality improvement methodology, being 
good mentors and mentees, and developing provider support groups. We anticipate a curriculum that will be 
delivered within one day to avoid keeping providers away from their work for extended periods for training. 
Recognizing, however, that stress and UB are deep-seated and difficult to change, we will plan for shorter 
booster sessions that may be delivered in-person or through a mobile-based technology platform. We will assess 
which components are relevant and acceptable, as well as appropriate length of training in the formative 
research.  
 
Training providers is important, but not enough. Thus, the intervention will employ an ecological perspective 
(Figure 2), which recognizes that behavior is affected by multiple levels of influence,24 to create an enabling 
environment for individual behavior change efforts. Potential strategies at each level include: (1) Engaging 
leadership to align the goals of addressing provider stress and UB with other system goals, to increase buy in 
and sustainability. (2) Using embedded champions who will help transmit ideas, pilot changes, and inspire 
others to adopt the principles and practices learnt. This will be achieved with a “Train the Trainer” approach, 
with potential trainers selected from within the health system, to create a flexible and efficient model for 
generating embedded and sustained support. (3) Peer Support and mentorship: Finally, we will facilitate the 
formation of support groups and a mentorship program. This operates at the interpersonal level and will help 
increase motivation, reduce sense of isolation among providers, and provide reinforcement for individual 
behavior change. 
 
Table 1: Intervention strategies  
1. Training  
We will develop a training for providers that addresses the following topics 
• 
Stress & positive coping mechanisms 
• 
Unconscious bias awareness and mitigation 
• 
Person-centered maternity care 
• 
Dealing with difficult situations 
• 
Teamwork and communication; Supportive supervision 
• 
Assertiveness; problem solving and growth mindset 
• 
Cultural humility & responsiveness; Compassion & dependability 
• 
Resilience & recovery; Safety & stability; Collaboration & empowerment 
As part of the PTBi work in Migori, providers were introduced to simulation training for the 
management of obstetric and neonatal emergencies as well as teamwork and communication. We 
are thus working with PRONTO international to integrate new content into their curriculum that 
addresses the above issues. This will lead to the development of an integrated curriculum that will 
improve quality of care by addressing the key drivers of poor person-centered care as well as the 
technical aspects of care. We anticipate an initial large group training (about 20 providers per 
group) in a one- or two-day training followed by monthly updates provided through smaller 
groups such as the peer support groups.  
 
2. Peer support and mentorship 
In the formative research, we will identify what works best for these groups in terms of group 
composition, size, and how the groups want to interact. In addition, we will work with providers to 
identify mentor/mentee pairs, based on on what providers said they will like in mentorship 
relationships 
 
3. Leadership engagement 
To ensure leadership buy in, support and sustainability of the intervention, we will engage 
leadership of the County from the beginning. This will include sharing findings from CPIPE1 with 
them and sharing our intervention in response to the findings as well as discussing issues 
identified from that are beyond the capability of our study. In addition, we will invite some of 

  
Page 11 of 28 
 
 
AIM 2 (Year 2): 
Piloting 
the 
intervention 
in 
Aim 1 to assess its 
feasibility 
and 
acceptability: We will implement the intervention in two high or medium delivery volume facilities in two sub-
counties in Migori County. All providers working in maternity units in selected facilities and lower level 
facilities that refer to them will be eligible for the pilot intervention. We anticipate about 20 providers per sub-
county (based on estimates from the PTBi work in Migori), giving about 40 providers in the intervention group. 
We will recruit health workers through letters to county, sub-county, and facility leadership, and fliers at health 
facilities. The ability to implement the intervention will be the first assessment of feasibility.  
 
In addition, we will document all steps of the implementation process to assess feasibility of various 
intervention components, implementation challenges and successes, and lessons learnt. To assess acceptability, 
we will interview providers who participate 
in 
the 
intervention on their experiences, their 
assessment 
of 
its 
relevance, 
and 
their 
interest, 
engagement, 
and satisfaction with the content and 
delivery. 
We 
will also interview leaders on their 
perceptions of 
the 
intervention 
and 
suggestions 
for 
improvement. 
We will conduct a process evaluation to 
assess 
intervention 
delivery, 
coverage, 
and 
integrity. 
Proctor’s framework will be used to 
evaluate 
implementation 
outcomes, 
using 
both 
quantitative 
and qualitative methods.69,70 We will assess 
intervention 
fidelity—adherence, exposure, quality of 
delivery, 
competence, 
participant 
responsiveness, 
and 
program 
differentiation—through interviews with 
participants, 
observations, 
and 
review 
of 
project 
documents, 
logs and attendance sheets (example of 
measures 
in 
Table 2).71,72  
 
AIM 3 (Yrs 2&3): Assess preliminary 
effect of the 
intervention on: (a) Provider knowledge, 
attitudes, and 
behaviors 
related 
to 
stress 
and 
unconscious 
bias; and (b) Provider stress levels. We 
will 
use 
a 
pretest-posttest control group design, with 
the 
two 
intervention facilities matched to the two 
control 
facilities, to assess the preliminary effect of 
the 
intervention. Matching criteria will include delivery volumes and staffing capacities of the facilities, as well as 
other relevant factors. We will recruit about 40 providers from the selected control facilities in addition to the 
40 providers in the intervention group for the evaluation (about 80 total). Randomizing individual providers is 
not ideal for this pilot because of a high potential for contamination. The pretest-posttest control group design 
allows us to address common threats to internal validity.73 If the intervention is shown to be effective and 
providers in the control group wish to participate in the intervention, we will consider offering the intervention 
to the control group after completing the post-test. This will be decided after the implementation of the pilot. 
 
them to serve on the advisory board. We will also plan to provide periodic updates during county 
health management meeting for ongoing engagement.  
 
4. Embedded champions 
To facilitate ongoing engagement, we will identify potential local leaders, and invite them to 
training where they will be taught how to facilitate peer support groups and serve as champions 
within their facilities  
Table 2: Potential evaluation measures 
Feasibility: Challenges, successes, lessons learnt 
Acceptability: Experiences with the training, 
assessment of its relevance, satisfaction with 
content and delivery, suggestions for improvement  
Fidelity: Number of providers trained, number 
trained as trainers, number of leaders engaged, 
hours of training, content delivered, etc.  
Knowledge: Practical questions based on the 
foundational learning related to stress, coping, and 
resilience; UB; PCMHC 
Attitudes: Attitudes towards coping with stress, 
support for suggested changes to reduce stress and 
UB, IAT scores, commitment to change, and 
attitudes towards providing PCMHC 
Behavior: Implementing coping mechanisms to 
reduce stress; vignettes with scenarios that may be 
influenced by individual bias 
Stress: Psychological and biological measures of 
stress and burnout: e.g., Stress score-based Cohen 
stress scale and hair cortisol levels 
Controls: Individual: Age, sex, education, marital 
status, income; position in facility, working hours 
per week, years of service, etc.; Institutional: 
adapted organizational climate surveys.  

  
Page 12 of 28 
Data management  
(a) Data collection procedures and measures:  
We will use or adapt existing tools as well as develop additional questions to measure provider knowledge, 
attitudes, and behaviors (Table 2). Given the deeply seated nature of UB, it may not change in the six-month 
period for the evaluation. Providers behavior may, however, change with awareness of their biases.58,64 To 
measure how bias affects care, we will use the Implicit Association Test (IAT) and vignettes with different 
scenarios that require judgment that may be influenced by individual bias, as has been used in other studies and 
in CPIPE1.59,60 We will use  
the validated psychological stress measures (Cohen stress scale74 and Shirom-Melamed  
Burnout scale75) used in CPIPE1 to measure provider stress.74 These measures will be included in a 
questionnaire and administered through surveys before and after the intervention for both the intervention and 
control arms. We will also measure heart rate variability (HRV) and collect hair samples before and after the 
intervention to analyze for hair cortisol levels. HRV is a non-invasive measure of stress, but readings are easily 
affected by physiological factors.76–78 Hair cortisol, on the other hand, is a more stable measure.79,80 It has less 
diurnal variation and does not require multiple samples a day. Different segments of hair will enable us to 
examine monthly changes in provider stress levels before and after the training.79 We assessed the feasibility of 
both measures in CPIPE1. We found that although both could be obtained, it was not always possible to get hair 
samples from all providers because some of them have their hair cut too short. Using the two measures helps 
ensure we have some physiologic measure for each person. We will also collect demographic and work-related 
data from all participants, as well as measures of organizational climate, to control for individual and 
organizational factors that might explain intervention outcomes. The pretest (T1) will be right before the 
intervention start and the posttest (T2) in about the 6th month, with other intervention activities in the 
intervening period.  
 
(b) Data collection and storage 
Interviews 
All data collection will occur in private spaces to ensure confidentiality during data collection. Quantitative 
survey data will be collected using tablets and the RedCap application and uploaded to secure UCSF servers, 
which will provide long-term storage for research data. This data is then downloaded and merged with other 
data described below for analysis. Qualitative data will be tape-recorded, and recorders stored in secured 
cabinets till transferred to encrypted box folders, after which data will be deleted from tapes. An experienced 
transcriptionist will transcribe the qualitative data, and all identifying information will be excluded from the 
transcription files. Only the study staff will have access to the data. All paper records, including consent forms, 
will be kept under lock and key with restricted access.   
 
Implicit association test (IAT) 
Following each interview, providers will be asked to fill out the IAT. The IAT is a test that has been validated 
to measure implicit biases, and has been used in many different settings.83,84 It measures the strength of 
associations between concepts (e.g., black people, poor people) and evaluations (e.g., good, bad) or stereotypes 
(e.g., athletic, clumsy). The main idea is that making a response is easier when closely related items share the 
same response key. When doing an IAT people are asked to quickly sort words into categories that are on the 
left and right hand side of the computer screen by pressing the “e” key if the word belongs to the category on 
the left and the “i” key if the word belongs to the category on the right. In this study, we will be using the IAT 
to examine evaluations and stereotypes based on people’s socioeconomic status, which we developed as part of 
CPIPE 1. The IAT is programmed with a software called Inquisit Lab that stores the data generated together 
with a respondent ID which is entered before the initiation of the test. A spreadsheet containing the HRV by ID 
numbers is then downloaded from the app and merged with the survey data for analysis. 
 
Heart rate variability 

  
Page 13 of 28 
HRV will be measured using a heart rate variability monitor (polar H10 heart rate sensor) which is strapped to 
the chest or worn on the wrist. We will obtain 5-minute HRV measurements with the provider in the seated 
position. The readings are transmitted via blue tooth to a secure phone app (Elite HRV app) and a respondent ID 
entered for each reading. No identifying information is entered in the app. A spreadsheet containing the HRV 
by ID numbers is then downloaded from the app and merged with the survey data for analysis. 
 
Hair cortisol  
For the hair samples, a few strands of hair (about 30 strands which is very little and respondents will be shown picture 
to get a sense of what 30 strands look like) will be taken from the scalp at back of the head. The hair samples will 
be wrapped in foil and labelled and put in envelopes or ziplock bags, and then be stored in in a locked cabinet 
until transported for analysis. The Hair samples will be labeled with unique codes and stored securely until 
transported for hair cortisol analysis in a laboratory capable of such analysis, and appropriately disposed after 
the analysis. To our knowledge there is no certified lab doing the hair cortisol analysis in Kenya at the moment. 
Thus, we plan to take the samples to US where there are labs for such analysis. We will provide the lab with 
deidentified samples labelled by respondent IDs, which they will analyze and provide us with the hair cortisol levels for 
each respondent in a spreadsheet which we will merge with the other quantitative data for analysis.  
 
(c)Data management and analysis plan  
 
Aims 2&3: We will record all in-depth interviews and transcribe them verbatim. We will use the REDCap 
application to conduct the quantitative surveys. The data collected with this application is uploaded directly to a 
secure online server and immediately available for analysis. This will enable us to do data quality checks while 
the data is being collected to address any issues in a timely manner. We will ensure confidentiality of all 
participants in the data collection and storage. 
 
We will use thematic analysis to analyze the qualitative data on feasibility and acceptability. We will use Braun 
and Clarks’ approach to thematic analysis. This is an iterative process getting familiar with the data, generating 
initial codes, searching for themes, reviewing themes, defining and naming themes, and producing the report. 
We will use the Dedoose software for coding the data.  
 
For the quantitative data, the merged dataset will be imported into Stata for analysis. We will examine the 
univariate and bivariate distributions of all variables and conduct appropriate multivariate analysis: logistic 
regressions for categorical outcomes and linear regression for continuous outcomes. We will use a difference in 
differences approach to assess intervention effects: for a given outcome, the intervention effect is the outcome 
change from baseline to end line for the intervention group (T2i -T1i) minus that for the control group (T2c - 
T1c).73 Where applicable, we will use mediation and moderation analyses to examine intervening and 
conditional effects. We will use intent to treat design, which is recommended for understanding intervention 
effectiveness.  
 
 
Ethical considerations 
Ethical approvals will be sought from the KEMRI Scientific and Ethics Review Unit (SERU) and the UCSF 
Committee for Human Research. Furthermore, authority to carry out research will be sought from the Migori 
County, Department of Health and participating facilities. 
 
Human Subjects:  

  
Page 14 of 28 
“First, do no harm”: We do not anticipate any harm to participants, as the risk that may be experienced is 
minimal. Participants may feel anxious about answering questions about stress and bias or having their 
physiological stress levels measured. We will train interviewers to be compassionate and sensitive. 
Direct Benefit: We think participation in the intervention might benefit providers by helping them to better 
manage stress which can improve their work experience. However, there may be no direct, immediate benefits 
to participants. But, the project will provide information that may inform future quality improvement efforts 
that address provider’s concerns. There also is no compensation for participation, as they will only receive a 
token—300KES in appreciation for their participation. 
Informed Consent: For all participants, the interviewers will provide information about the study and obtain 
their written consent before initiating data collection. We will let them know that participation in the study is 
voluntary and will not affect their positions in any way. Participants will be given adequate information will 
have the option to participate in the surveys but decline to have their heart rate variability measured and or give 
their hair samples. Informed consent documents for interviews are attached.  
Confidentiality: All data collection will occur in private spaces to ensure confidentiality during data collection. 
Electronic data will be stored on secured computers and UCSF servers. All paper records, including consent 
forms, will be kept under lock and key with restricted access. 
 
Study limitation 
A limitation of this intervention is that it does not address structural factors such as shortage of health workers 
and essential supplies that affect provider stress and PCMHC. These are important but beyond the scope of an 
individual intervention. This intervention is to help providers cope and provide better care in the midst of the 
structural deficiencies. It will complement efforts by government and other organizations to address the 
structural issues. Additionally, the problem-solving strategies learnt from the intervention will help address 
artificial shortages such as due unbalanced scheduling. Furthermore, reducing burnout has the potential to 
reduce shortages from burnout related absenteeism. 
 
Expected application of the results  
We will use the pilot data to refine the intervention and develop an R01 proposal for a rigorous multisite 
evaluation with a larger sample and longer follow up, to assess the effect of the intervention on PCMHC 
(measured from the perspective of women with the PCMHC scale).81 If the curriculum is proven to be effective, 
it will be disseminated to the Kenyan Ministry of Health for continuous workforce training through refreshers 
and as part of the formative training for health trainees. This research will address a major gap in the efforts to 
improve MCH outcomes and reduce disparities.  
 
Time frame/duration of the project 
The intervention design will be in year 1, pilot testing and evaluation in year 2, and analysis and revisions for 
the R01 in year 3 (Figure 3).  
 
  
Figure 3: Timeline for Research activities by quarter (Q) 
  
Year 1 
Year 2 
Year 3 
  
Q1 Q2 Q3 Q4 Q1 
Q2 Q3 Q4 Q1 
Q2 Q3 Q4 
Formative research 
  
  
  
  
  
  
  
  
  
  
  
  
Intervention design 
  
  
  
  
  
  
  
  
  
  
  
  
Pretest 
  
  
  
  
  
  
  
  
  
  
  
  
Piloting 
  
  
  
  
  
  
  
  
  
  
  
  
Posttest 
  
  
  
  
  
  
  
  
  
  
  
  
Analysis of pilot data 
  
  
  
  
  
  
  
  
  
  
  
  

  
Page 15 of 28 
R01 preparation 
  
  
  
  
  
  
  
  
  
  
  
  
Writing & Dissemination 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Role of Investigators  
 
Patience Afulani: Principal Investigator 
Dr. Afulani is an Assistant Professor at UCSF and the recipient of this transition to independence (K99/R00) 
grant. She is a Ghanaian trained physician with an MPH and PhD. She will lead all aspects of the project 
including the scientific and administrative conduct of this award. She will lead the development of the protocol, 
the data collection tools, and the intervention. Additionally, she will have responsibility for data analysis and 
interpretation, and dissemination of the research results. Dr. Afulani will ensure ethical conduct of the research 
and oversee the international subcontract.  
 
Linnet Ongeri: Co-PI 
Is a mental health researcher working at KEMRI. She will serve as the site PI and point of contact for KEMRI 
and facilitate obtaining local approvals for the project. In addition, she supports all project activities in Kenya 
including helping to identify and coordinate communication with key stakeholders in Kenya, participate in 
planning meetings, implementation activities, and dissemination activities.  As a health care professional in 
Kenya, she will help advocate for uptake of the intervention, if successful, to improve provider wellbeing as a 
means to improving quality of care.  
 
Joyceline Kinyua: Co-investigator 
She is a scientist at KEMRI. She has an MPH in epidemiology and an interest in social science research. She 
will support Dr. Ongeri in obtaining local approvals for the project. She will also support all project activities in 
Kenya including helping to identify and communicate with stakeholders and participating in planning meetings, 
implementation activities, and in dissemination activities.  
 
 
 
 
 

  
Page 16 of 28 
 
References 
 
1.  
WHO, UNICEF, UNFPA, The World Bank, United Nations Population Division. Trends in maternal 
mortality: 
1990 
to 
2013 
[Internet]. 
WHO. 
2014 
[cited 
2014 
Jul 
17]. 
Available 
from: 
http://www.who.int/reproductivehealth/publications/monitoring/9789241503631/en/index.html 
2.  
Gabrysch S, Campbell OM. Still too far to walk: Literature review of the determinants of delivery 
service use. BMC Pregnancy Childbirth. 2009 Aug 11;9(1):34. PMID: 19671156 
3.  
Moyer CA, Mustafa A. Drivers and deterrents of facility delivery in sub-Saharan Africa: a systematic 
review. Reprod Health. 2013;10:40. PMID: 23962135 
4.  
Graham WJ, McCaw-Binns A, Munjanja S. Translating Coverage Gains into Health Gains for All 
Women and Children: The Quality Care Opportunity. PLoS Med [Internet]. 2013 Jan [cited 2013 Jul 30];10(1). 
Available from: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3545868/ PMCID: PMC3545868 
5.  
van den Broek NR, Graham WJ. Quality of care for maternal and newborn health: the neglected agenda. 
BJOG Int J Obstet Gynaecol. 2009 Oct;116 Suppl 1:18–21. PMID: 19740165 
6.  
Bohren MA, Hunter EC, Munthe-Kaas HM, Souza JP, Vogel JP, Gülmezoglu AM. Facilitators and 
barriers to facility-based delivery in low- and middle-income countries: a qualitative evidence synthesis. Reprod 
Health. 2014;11(1):71. PMCID: PMC4247708 
7.  
Friberg IK, Kinney MV, Lawn JE, Kerber KJ, Odubanjo MO, Bergh A-M, Walker N, Weissman E, 
Chopra M, Black RE, Science in Action: Saving the lives of Africa’s Mothers, Newborns, and Children 
working group, Axelson H, Cohen B, Coovadia H, Diab R, Nkrumah F. Sub-Saharan Africa’s mothers, 
newborns, and children: how many lives could be saved with targeted health interventions? PLoS Med. 2010 
Jun;7(6):e1000295. PMID: 20574515 
8.  
Miller S, Abalos E, Chamillard M, Ciapponi A, Colaci D, Comandé D, Diaz V, Geller S, Hanson C, 
Langer A, Manuelli V, Millar K, Morhason-Bello I, Castro CP, Pileggi VN, Robinson N, Skaer M, Souza JP, 
Vogel JP, Althabe F. Beyond too little, too late and too much, too soon: a pathway towards evidence-based, 
respectful maternity care worldwide. The Lancet [Internet]. 2016 Sep [cited 2016 Sep 24]; Available from: 
http://linkinghub.elsevier.com/retrieve/pii/S0140673616314726 
9.  
Institute of Medicine. Crossing the Quality Chasm: A New Health System for the 21st Century 
[Internet]. 2001. Available from: http://www.iom.edu/Reports/2001/Crossing-the-Quality-Chasm-A-New-
Health-System-for-the-21st-Century.aspx 
10.  
Bohren MA, Vogel JP, Hunter EC, Lutsiv O, Makh SK, Souza JP, Aguiar C, Saraiva Coneglian F, Diniz 
ALA, Tunçalp Ö, Javadi D, Oladapo OT, Khosla R, Hindin MJ, Gülmezoglu AM. The Mistreatment of Women 
during Childbirth in Health Facilities Globally: A Mixed-Methods Systematic Review. PLoS Med. 2015 Jun 
30;12(6):e1001847.  
11.  
Abuya T, Warren CE, Miller N, Njuki R, Ndwiga C, Maranga A, Mbehero F, Njeru A, Bellows B. 
Exploring the Prevalence of Disrespect and Abuse during Childbirth in Kenya. PLoS ONE [Internet]. 2015 Apr 
17 [cited 2015 Aug 31];10(4). Available from: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4401776/ 
PMCID: PMC4401776 
12.  
Moyer CA, Adongo PB, Aborigo RA, Hodgson A, Engmann CM. “They treat you like you are not a 
human being”: maltreatment during labour and delivery in rural northern Ghana. Midwifery. 2014 
Feb;30(2):262–268. PMID: 23790959 
13.  
Tunçalp O, Hindin MJ, Adu-Bonsaffoh K, Adanu R. Listening to women’s voices: the quality of care of 
women experiencing severe maternal morbidity, in Accra, Ghana. PloS One. 2012;7(8):e44536. PMID: 
22952992 
14.  
Koblinsky M, Moyer CA, Calvert C, Campbell J, Campbell OMR, Feigl AB, Graham WJ, Hatt L, 
Hodgins S, Matthews Z, McDougall L, Moran AC, Nandakumar AK, Langer A. Quality maternity care for 
every woman, everywhere: a call to action. The Lancet [Internet]. 2016 Sep [cited 2016 Sep 24]; Available 
from: http://linkinghub.elsevier.com/retrieve/pii/S0140673616313332 

  
Page 17 of 28 
15.  
Ratcliffe HL, Sando D, Lyatuu GW, Emil F, Mwanyika-Sando M, Chalamilla G, Langer A, McDonald 
KP. Mitigating disrespect and abuse during childbirth in Tanzania: an exploratory study of the effects of two 
facility-based interventions in a large public hospital. Reprod Health. 2016;13:79.  
16.  
Ratcliffe HL, Sando D, Mwanyika-Sando M, Chalamilla G, Langer A, McDonald KP. Applying a 
participatory approach to the promotion of a culture of respect during childbirth. Reprod Health. 2016;13:80.  
17.  
Filby A, McConville F, Portela A. What Prevents Quality Midwifery Care? A Systematic Mapping of 
Barriers in Low and Middle Income Countries from the Provider Perspective. PLOS ONE. 2016 May 
2;11(5):e0153391.  
18.  
UNC Executive Development. The Real Effects of Unconscious Bias in the Workplace [Internet]. 2015 
[cited 2016 Oct 26]. Available from: http://execdev.kenan-flagler.unc.edu/blog/the-real-effects-of-unconscious-
bias-in-the-workplace-0 
19.  
Leape LL, Shore MF, Dienstag JL, Mayer RJ, Edgman-Levitan S, Meyer GS, Healy GB. Perspective: a 
culture of respect, part 1: the nature and causes of disrespectful behavior by physicians. Acad Med J Assoc Am 
Med Coll. 2012 Jul;87(7):845–852. PMID: 22622217 
20.  
Leape LL, Shore MF, Dienstag JL, Mayer RJ, Edgman-Levitan S, Meyer GS, Healy GB. Perspective: a 
culture of respect, part 2: creating a culture of respect. Acad Med J Assoc Am Med Coll. 2012 Jul;87(7):853–
858. PMID: 22622219 
21.  
Andersen HM. “Villagers”: Differential treatment in a Ghanaian hospital. Soc Sci Med. 2004;59:2003–
12.  
22.  
Glanz K, Rimer KB. Theory at a Glance: A Guide for Health Promotion Practice. National Cancer 
Institute, National Institutes of Health, U.S. Department of Health and Human Services. NIH Pub. No. 97-3896. 
Washington, DC: NIH. 1997.  
23.  
San Francisco Department of Public Health. Trauma Informed Systems Initiative 2014 Year in Review 
[Internet]. 2015. Available from: http://www.leapsf.org/pdf/Trauma-Informed-Systems-Initative-2014.pdf 
24.  
McLeroy KR, Bibeau D, Steckler A, Glanz K. An Ecological Perspective on Health Promotion 
Programs. Health Educ Behav. 1988 Jan;15(4):351–377.  
25.  
Alkema L, Chou D, Hogan D, Zhang S, Moller A-B, Gemmill A, Fat DM, Boerma T, Temmerman M, 
Mathers C, Say L. Global, regional, and national levels and trends in maternal mortality between 1990 and 
2015, with scenario-based projections to 2030: a systematic analysis by the UN Maternal Mortality Estimation 
Inter-Agency Group. The Lancet. 2016 Jan;387(10017):462–474.  
26.  
WHO. PMNCH Fact Sheet:  Maternal mortality [Internet]. 2011 [cited 2013 Jul 16]. Available from: 
http://www.who.int/pmnch/media/press_materials/fs/fs_mdg5_maternalmortality/en/index.html 
27.  
WHO. Making pregnancy safer: the critical role of the skilled attendant. Jt Statement WHO ICM FIGO 
Geneva. 2004;  
28.  
Khan KS, Wojdyla D, Say L, Gulmezoglu AM, Van Look PF. WHO analysis of causes of maternal 
death: a systematic review. Lancet. 2006;367:1066–1074.  
29.  
Say L, Chou D, Gemmill A, Tunçalp Ö, Moller A-B, Daniels J, Gülmezoglu AM, Temmerman M, 
Alkema L. Global causes of maternal death: a WHO systematic analysis. Lancet Glob Health. 2014 Jun 
1;2(6):e323–e333. PMID: 25103301, 25103301 
30.  
WHO. Global Health Observatory Data Repository, MDG 5: Maternal health: [Internet]. 2013 [cited 
2013 Sep 5]. Available from: http://apps.who.int/gho/data/node.main.REPWOMEN39 
31.  
March of Dimes, PMNCH, Save the Children. Born Too Soon: The Global Action Report on Preterm 
Birth. Eds CP Howson, MV Kinney, JE Lawn. World Health Organization, Geneva; 2012.  
32.  
Kenya National Bureau of Statistics, Ministry of Health, National AIDS Control Council, Kenya 
Medical Research Institute, National Council for Population and Development, Nairobi, Kenya, and The DHS 
Program, ICF International, Rockville, Maryland, USA. The DHS Program - Kenya: DHS, 2014 - Final Report 
(English) [Internet]. 2015 [cited 2016 Feb 18]. Available from: http://dhsprogram.com/publications/publication-
FR308-DHS-Final-Reports.cfm 

  
Page 18 of 28 
33.  
Anwar I, Kalim N, Koblinsky M. Quality of Obstetric Care in Public-sector Facilities and Constraints to 
Implementing Emergency Obstetric Care Services: Evidence from High- and Low-performing Districts of 
Bangladesh. J Health Popul Nutr. 2009 Apr;27(2):139–155. PMCID: PMC2761772 
34.  
Afulani PA, Kirumbi L, Lyndon A. What makes or mars the facility-based childbirth experience: 
thematic analysis of women’s childbirth experiences in western Kenya. Reprod Health. 2017 Dec 29;14(1):180. 
PMCID: PMC5747138 
35.  
Afulani PA, Moyer C. Explaining Disparities in Use of Skilled Birth Attendants in Developing 
Countries: A Conceptual Framework. PLOS ONE. 2016 Apr 22;11(4):e0154110.  
36.  
Fahy K. What is woman-centred care and why does it matter? Women Birth. 2012 Dec 1;25(4):149–
151.  
37.  
Pope R, Graham L, Patel S. Woman-centred care. Int J Nurs Stud. 2001 Apr;38(2):227–238.  
38.  
Downe S, Lawrie TA, Finlayson K, Oladapo OT. Effectiveness of respectful care policies for women 
using routine intrapartum services: a systematic review. Reprod Health. 2018 Feb 6;15(1):23. PMCID: 
PMC5801845 
39.  
Rubashkin N, Warnock R, Diamond-Smith N. A systematic review of person-centered care 
interventions to improve quality of facility-based delivery. Reprod Health. 2018 Oct 10;15(1):169. PMCID: 
PMC6180507 
40.  
Abuya T, Ndwiga C, Ritter J, Kanya L, Bellows B, Binkin N, Warren CE. The effect of a multi-
component intervention on disrespect and abuse during childbirth in Kenya. BMC Pregnancy Childbirth. 2015 
Sep 22;15(1):224. PMID: 26394616 
41.  
Familoni O. An overview of stress in medical practice. Afr Health Sci. 2008 Mar;8(1):6–7. PMCID: 
PMC2408543 
42.  
Yeboah MA, Ansong MO, Antwi HA, Yiranbon E, Anyan F, Gyebil F. Determinants of Workplace 
Stress among Healthcare Professionals in Ghana: An Empirical Analysis. ResearchGate. 2014 Mar 1;Vol. 5(No. 
4 [Special Issue]):140–151.  
43.  
Cooper LA, Roter DL, Carson KA, Beach MC, Sabin JA, Greenwald AG, Inui TS. The Associations of 
Clinicians’ Implicit Attitudes About Race With Medical Visit Communication and Patient Ratings of 
Interpersonal Care. Am J Public Health. 2012 May;102(5):979–987. PMCID: PMC3483913 
44.  
Nosek BA, Ranganath KA, Smith CT, Chugh D, Olson KR, Lindner NM, Greenwald AG, Devos T, 
Banaji M, Smyth FL, Hansen JJ. Pervasiveness and Correlates of Implicit Attitudes and Stereotypes. 2007 
[cited 2016 Dec 8]; Available from: https://dash.harvard.edu/handle/1/2958438 
45.  
Blair IV, Steiner JF, Havranek EP. Unconscious (Implicit) Bias and Health Disparities: Where Do We 
Go from Here? Perm J. 2011;15(2):71–78. PMCID: PMC3140753 
46.  
Major B, Mendes WB, Dovidio JF. Intergroup relations and health disparities: a social psychological 
perspective. Health Psychol Off J Div Health Psychol Am Psychol Assoc. 2013 May;32(5):514–524. PMCID: 
PMC3988903 
47.  
Chapman EN, Kaatz A, Carnes M. Physicians and implicit bias: how doctors may unwittingly 
perpetuate health care disparities. J Gen Intern Med. 2013 Nov;28(11):1504–1510. PMCID: PMC3797360 
48.  
McEwen BS. Stressed or stressed out: What is the difference? J Psychiatry Neurosci. 2005 
Sep;30(5):315–318. PMCID: PMC1197275 
49.  
Public Health England. Interventions to prevent burnout in high risk individuals: evidence review - 
Publications 
- 
GOV.UK 
[Internet]. 
2016 
[cited 
2016 
Oct 
26]. 
Available 
from: 
https://www.gov.uk/government/publications/interventions-to-prevent-burnout-in-high-risk-individuals-
evidence-review 
50.  
Halbesleben JRB, editor. Handbook of Stress and Burnout in Health Care. 3 edition. New York: Nova 
Science Pub Inc; 2008.  
51.  
Wallace JE, Lemaire JB, Ghali WA. Physician wellness: a missing quality indicator. Lancet Lond Engl. 
2009 Nov 14;374(9702):1714–1721. PMID: 19914516 
52.  
Klein J, Frie KG, Blum K, Knesebeck O von dem. Burnout and perceived quality of care among 
German clinicians in surgery. Int J Qual Health Care. 2010 Dec 1;22(6):525–530. PMID: 20935011 

  
Page 19 of 28 
53.  
Krasner MS, Epstein RM, Beckman H, Suchman AL, Chapman B, Mooney CJ, Quill TE. Association of 
an educational program in mindful communication with burnout, empathy, and attitudes among primary care 
physicians. JAMA. 2009 Sep 23;302(12):1284–1293. PMID: 19773563 
54.  
Shanafelt TD, Sloan JA, Habermann TM. The well-being of physicians. Am J Med. 2003 Apr 
15;114(6):513–519.  
55.  
Khalil D. Violence against midwives in Cape Town. Afr J Midwifery Womens Health. 2009 Feb 
1;3(1):37–40.  
56.  
Fauveau V, Sherratt DR, de Bernis L. Human resources for maternal health: multi-purpose or 
specialists? Hum Resour Health. 2008 Sep 30;6:21. PMCID: PMC2569064 
57.  
Afulani PA, Kelly AM, Buback L, Asunka J, Kirumbi L, Lyndon A. Providers’ perceptions of disrespect 
and abuse during childbirth: a mixed-methods study in Kenya. Health Policy Plan [Internet]. 2020 Mar 10 [cited 
2020 Mar 10];(czaa009). Available from: https://doi.org/10.1093/heapol/czaa009 
58.  
Mendes WB, Koslov K. Brittle smiles: positive biases toward stigmatized and outgroup targets. J Exp 
Psychol Gen. 2013 Aug;142(3):923–933. PMID: 22889160 
59.  
Green AR, Carney DR, Pallin DJ, Ngo LH, Raymond KL, Iezzoni LI, Banaji MR. Implicit bias among 
physicians and its prediction of thrombolysis decisions for black and white patients. J Gen Intern Med. 2007 
Sep;22(9):1231–1238. PMCID: PMC2219763 
60.  
Sabin JA, Greenwald AG. The influence of implicit bias on treatment recommendations for 4 common 
pediatric conditions: pain, urinary tract infection, attention deficit hyperactivity disorder, and asthma. Am J 
Public Health. 2012 May;102(5):988–995. PMCID: PMC3483921 
61.  
Turan JM, Hatcher AH, Medema-Wijnveen J, Onono M, Miller S, Bukusi EA, Turan B, Cohen CR. The 
Role of HIV-Related Stigma in Utilization of Skilled Childbirth Services in Rural Kenya: A Prospective Mixed-
Methods Study. Bangsberg DR, editor. PLoS Med. 2012 Aug 21;9(8):e1001295.  
62.  
Turan JM, Bukusi EA, Onono M, Holzemer WL, Miller S, Cohen CR. HIV/AIDS Stigma and Refusal of 
HIV Testing Among Pregnant Women in Rural Kenya: Results from the MAMAS Study. AIDS Behav. 2011 
Aug 1;15(6):1111–1120.  
63.  
Tincher MM, Lebois LAM, Barsalou LW. Mindful Attention Reduces Linguistic Intergroup Bias. 
Mindfulness. 2016 Apr 1;7(2):349–360.  
64.  
Devine PG, Forscher PS, Austin AJ, Cox WTL. Long-term reduction in implicit race bias: A prejudice 
habit-breaking intervention. J Exp Soc Psychol. 2012 Nov;48(6):1267–1278.  
65.  
Clarke AE. Situational Analysis: Grounded Theory After the Postmodern Turn. 1 edition. Thousand 
Oaks, Calif: SAGE Publications, Inc; 2005.  
66.  
Lateef F. Simulation-based learning: Just like the real thing. J Emerg Trauma Shock. 2010;3(4):348–
352. PMCID: PMC2966567 
67.  
Sally S. Russell. An Overview of Adult Learning Processes [Internet]. Medscape. [cited 2016 Nov 7]. 
Available from: http://www.medscape.com/viewarticle/547417 
68.  
Bloom SL. Organizational Stress and Trauma-Informed Services. In: Levin BL, Becker MA, editors. 
Public Health Perspect Womens Ment Health [Internet]. Springer New York; 2010 [cited 2016 Dec 6]. p. 295–
311. Available from: http://link.springer.com/chapter/10.1007/978-1-4419-1526-9_15 
69.  
Proctor EK, Powell BJ, Baumann AA, Hamilton AM, Santens RL. Writing implementation research 
grant proposals: ten key ingredients. Implement Sci. 2012;7:96.  
70.  
Proctor E, Silmere H, Raghavan R, Hovmand P, Aarons G, Bunger A, Griffey R, Hensley M. Outcomes 
for implementation research: conceptual distinctions, measurement challenges, and research agenda. Adm 
Policy Ment Health. 2011 Mar;38(2):65–76. PMCID: PMC3068522 
71.  
Carroll C, Patterson M, Wood S, Booth A, Rick J, Balain S. A conceptual framework for 
implementation fidelity. Implement Sci. 2007 Nov 30;2:40.  
72.  
Breitenstein SM, Gross D, Garvey C, Hill C, Fogg L, Resnick B. Implementation Fidelity in 
Community-Based Interventions. Res Nurs Health. 2010 Apr;33(2):164–173. PMCID: PMC3409469 
73.  
Rossi PH, Lipsey MW, Freeman HE. Evaluation: A systematic approach. 7th ed. Thousand Oaks, CA: 
Sage Publications, Inc.; 2004:68-81.  

  
Page 20 of 28 
74.  
Cohen S, Kamarck T, Mermelstein R. A global measure of perceived stress. J Health Soc Behav. 1983 
Dec;24(4):385–396. PMID: 6668417 
75.  
Gerber M, Colledge F, Mücke M, Schilling R, Brand S, Ludyga S. Psychometric properties of the 
Shirom-Melamed Burnout Measure (SMBM) among adolescents: results from three cross-sectional studies. 
BMC 
Psychiatry 
[Internet]. 
2018 
Aug 
25 
[cited 
2018 
Nov 
26];18. 
Available 
from: 
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6109266/ PMCID: PMC6109266 
76.  
Kim H-G, Cheon E-J, Bai D-S, Lee YH, Koo B-H. Stress and Heart Rate Variability: A Meta-Analysis 
and Review of the Literature. Psychiatry Investig. 2018 Mar;15(3):235–245. PMCID: PMC5900369 
77.  
Järvelin-Pasanen S, Sinikallio S, Tarvainen MP. Heart rate variability and occupational stress-systematic 
review. Ind Health. 2018 Nov 21;56(6):500–511. PMID: 29910218 
78.  
Lennartsson A-K, Jonsdottir I, Sjörs A. Low heart rate variability in patients with clinical burnout. Int J 
Psychophysiol. 2016 Dec 1;110:171–178.  
79.  
Russell E, Koren G, Rieder M, Van Uum S. Hair cortisol as a biological marker of chronic stress: 
Current status, future directions and unanswered questions. Psychoneuroendocrinology. 2012 May;37(5):589–
601.  
80.  
Sauvé B, Koren G, Walsh G, Tokmakejian S, Van Uum SHM. Measurement of cortisol in human hair as 
a biomarker of systemic exposure. Clin Investig Med Med Clin Exp. 2007;30(5):E183-191. PMID: 17892760 
81.  
Afulani PA, Diamond-Smith N, Golub G, Sudhinaraset M. Development of a tool to measure person-
centered maternity care in developing settings: validation in a rural and urban Kenyan population. Reprod 
Health 
[Internet]. 
2017 
Sep 
22 
[cited 
2019 
Feb 
26];14. 
Available 
from: 
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5610540/ PMCID: PMC5610540 
82  
Afulani PA, Buback L, Kelly AM, Kirumbi L, Cohen CR, Lyndon A. Providers’ perceptions of 
communication and women’s autonomy during childbirth: a mixed methods study in Kenya. Reprod Health. 
2020 Jun 3;17(1):85.  
83. Lane, K. A., Banaji, M. R., Nosek, B. A. & Greenwald, A. G. Understanding and Using the Implicit 
Association Test: IV: What We Know (So Far) about the Method. in Implicit measures of attitudes (eds. 
Wittenbrink, B. & Schwarz, N.) 59–102 (Guilford Press, 2007). 
84. About the IAT. Available at: https://implicit.harvard.edu/implicit/iatdetails.html. \ 
 
 

  
Page 21 of 28 
 
 
STATISTICAL REPORT AND ANALYSIS PLAN 
 
Study Title: Addressing Provider Stress and Unconscious Bias to Improve Quality of Maternal Health Care: 
Caring for Providers to Improve Patient experience (CPIPE) study phase 2 in Migori County 
 
Sep 28, 2020 V3 Protocol 
 
Study Acronym: CPIPE 2 
 
ClinicalTrials.gov Identifier: NCT05019131  
 
 
 
Summary 
Poor person-centered maternal health care (PCMHC) contributes to high maternal and neonatal mortality in 
sub-Saharan Africa (SSA), and disparities in PCMHC are driving disparities in use of maternal health services., 
However, little research exists on how to improve PCMHC and reduce disparities. We seek to fill this gap with 
this project. We propose targeting health provider stress and unconscious bias as fundamental factors driving 
both poor PCMHC and disparities in PCMHC. Health care provider stress and unconscious bias are important 
to consider because: (1) providers in low-resource settings often work under very stressful conditions; (2) 
unconscious bias is prevalent in every society including SSA; and (3) these factors are mutually reinforcing 
drivers of poor quality care and disparities in person-centered care. In the first phase of the project (CPIPE1), 
we conducted research to examine (1) the factors associated with PCMHC and identified provider stress and 
unconscious bias as key contributing factors. We also examined the levels of provider stress and unconscious 
bias and the types of stressors and biases in Migori County, Kenya. The results of that research will be used to 
inform this phase (CPIPE2), the aims of which are to: (1) design a multicomponent theory and evidence-based 
intervention that enables providers to identify and manage their stress and unconscious bias; (2) pilot the 
intervention to assess its feasibility and acceptability; and (3) assess preliminary effect of the intervention on: 
(a) provider knowledge, attitudes, and behaviors related to stress and unconscious bias; and (b) provider 
stress levels using a pretest-posttest control group design. We will use the results of the pilot to refine the 
intervention and develop an R01 proposal for a multi-site evaluation with a larger sample and longer follow up 
to assess impact on PCMHC. This study will yield valuable information to inform quality improvement efforts for 
PCMHC. 
 
General Objectives: To develop and test a locally delivered intervention for addressing provider stress and 
unconscious bias among maternal health providers in Migori, Kenya.  
 
Specific objective 
1. To design a multicomponent theory and evidence-based intervention that enables providers to identify 
and manage their stress and unconscious bias in Migori County 
2. To pilot the intervention developed in Aim 1 to assess its feasibility and acceptability 
3. To assess preliminary effect of the intervention on: (a) providers’ knowledge, attitudes, and behaviors 
related to stress and unconscious bias; and (b) provider stress levels.  
 
Target Population: Health care providers and support staff in maternity units in health facilities in Migori 
County. 
 
Inclusion criteria: All providers working in maternity units, inclusive of clinical staff (nurses, midwives, doctors, 
clinical officers) and support staff (nurse aides, cleaners), in the selected study facilities in Migori County 

  
Page 22 of 28 
 
Exclusion criteria: Intend to leave study facility within intervention period; and Inability to participate in 
intervention activities.  
 
Study Design: A pretest-posttest non-equivalent control group design  
 
Number of Participants: 80 participants in total; 40 from control health facilities and 40 from intervention 
facilities. 
Study aims 
 
AIM 1: Design a multicomponent theory and evidence-based intervention that enables providers to identify and 
manage their stress and unconscious bias. The intervention design will be an iterative process informed by 
formative research and continuous feedback in consultation with key stakeholders and experts. About 20 
stakeholders including county and sub-county health officials, facility and unit heads, and different cadres 
health care providers will be interviewed. Qualitative research will inform the adaptation of the intervention 
using implementation science methodology. The intervention will be informed by the Social Cognitive Theory,22 
Trauma Informed System framework,23 and the Ecological Perspective.24 It will include provider training using 
adult learning concepts and simulation to engage providers, increase self-efficacy, and stimulate behavior 
change; as well as foster leadership engagement, and use of embedded champions to create an enabling 
environment for individual behavior change.  
 
AIM 2: Pilot the intervention developed in Aim 1 to assess its feasibility and acceptability: We will pilot the 
intervention with about 40 health workers (doctors, clinical officers, nurses, and midwives) in Migori County, 
Kenya. In the formative phase, we will assess the feasibility and acceptability of including different cadres of 
health providers. We will collect data on process measures and document all steps of the implementation 
process to assess feasibility and acceptability of various components of the intervention, and to assess 
intervention fidelity. We will use quantitative and qualitative methods to evaluate the implementation process.  
 
AIM 3: Assess preliminary effect of the intervention on: (a) Providers’ knowledge, attitudes, and behaviors 
related to stress and unconscious bias; and (b) Provider stress levels. We will use a pretest-posttest non-
equivalent control group design with quantitative measures of provider knowledge, attitudes, and behaviors 
related to stress and unconscious bias to assess the preliminary effect of the intervention on these outcomes. 
We will also assess preliminary effectiveness on provider stress levels using validated psychological measures 
of stress and biomarkers.  
 
The Statistical Analysis Plan below pertains to Aim 3/objective 3. 
 
Definition of Study Variables 
 
a. 
 Primary outcomes 
We have four primary outcomes; 1) perceived stress, 2) burnout, 3)Stress knowledge, and 4) unconscious bias 
knowledge and attitude score. These variables are described in box 1 
b. 
Secondary outcomes 
We also have four secondary outcomes: 1) Hair cortisol levels, 2) Hair rate variability, 3) IAT, and 4) explicit 
bias score. A brief description of these outcome variables are provided in Box 2. 
Box 1: Study Primary outcome measures 
Perceived 
The 10-item Cohen Perceived Stress Scale (PSS) on people’s feelings and thoughts 

  
Page 23 of 28 
 
 
 
 
Stress 
in the past month.3 The PSS has undergone substantial testing for validity and 
reliability in different populations including in the study setting.4–7 These questions 
capture how nervous or stressed, unpredictable, uncontrollable, and overloaded 
respondents find their lives; response scales are 0 (never) to 4 (very often). 
Summative scores will be created after recoding all response to the same direction. 
Scores range from 0 to 40; higher scores indicate higher perceived stress.  
Burnout 
The14-item Shirom-Melamed Burnout Measure (SMBM) on feelings at work in the 
past month.8 The SMBM has undergone psychometric testing in various populations 
with strong evidence for its validity and reliability in the study setting. 4–6,8–10 
Questions capture three domains: physical fatigue, emotional exhaustion, and 
cognitive weariness, with responses ranging from 1 (never or almost never) to 7 
(always or almost always). Mean scores will be generated and will range from 1 to 7; 
higher scores indicate higher burnout. 
Stress 
knowledge 
and attitudes  
A 14-question knowledge of stress and stress management assessment adapted 
from existing questions.1 Each multiple-choice question has one correct answer; 
responses will be coded into binary (right or wrong) and summed to create a 
composite stress knowledge score. Scores range from 0 to 14; higher scores indicate 
higher stress knowledge.  
Unconscious 
bias 
knowledge 
and attitude 
A 17-question assessment of unconscious bias adapted from existing questions 2. 
Each multiple-choice question has one correct answer; responses will be coded into 
binary (right or wrong) and summed to create a composite implicit bias knowledge 
and attitude score. Scores range from 0 to 17; higher scores indicate higher implicit 
bias knowledge.  
Box 2: Secondary outcome measures 
Hair 
cortisol 
levels 
Hair cortisol reflect cumulative secretion of cortisol within the hair growth period, with 
high levels indicative of chronic or sustained stress.11,12We will obtain hair samples 
from providers who have enough hair (at least 2cm long) and willing to provide hair 
samples. Hair samples will then be processed to obtain cortisol levels. There are no 
specified cut-offs for cortisol levels, but, on average, higher cortisol levels indicate 
higher stress. 
Hair rate 
variability 
HRV is a measure of the beat-to-beat interval between consecutive heart beats to 
estimate cardiac vagal tone.13,14 HRV will be measured using the CorSense finger 
monitor by Elite HRV,15 and the natural log of the root mean square of successive 
differences in RR intervals (lnRMSSD) calculated.13 The LnRMSSD reflect 
parasympathetic nervous system activity. Higher LnRMSSD is thus more adaptive 
and associated with relaxation and calm states, while low levels are associated with 
anxiety, high stress, and burnout 14,16. 
IAT 
Two situationally specific vignettes assessing providers’ perceptions of women’s 
PCMC expectations and behaviors based on SES 17,18. One vignette describes a 
woman with low SES and the other vignette a high SES woman; vignettes were read 
in counter-balanced orders to providers and followed by 8-questions assessing 
providers’ perceptions of the woman’s expectations for introductions, consenting, 
companionship, potential to cooperate, understand explanations, and exaggerate 
pain; and provider behavior needed to convey seriousness and gain patient 
cooperation. The vignettes and questions were informed by measurement of explicit 
bias in prior literature 19,20 and prior research in this setting 21–23,19,20. Response options 

  
Page 24 of 28 
range from strongly disagree to strongly agree on a 4-point scale. Summative scores 
are created after recoding all responses to the same direction; higher scores indicate 
higher bias towards the woman in the vignette. Scores range from 0 to 27. Cronbach 
alpha for the 8 items in the study sample at baseline was 0.70 for the low SES 
vignette and 0.80 for the high SES vignette. 
Explicit 
bias score. 
 
Situationally specific vignettes assessing providers’ perceptions of women’s PCMC 
expectations and behaviors based on SES will be used to measure explicit bias 17,18. 
One vignette describes a woman with low SES and the other vignette a high SES 
woman; vignettes were read in counter-balanced orders to providers and followed by 
8-questions assessing providers’ perceptions of the woman’s expectations for 
introductions, consenting, companionship, potential to cooperate, understand 
explanations, and exaggerate pain; and provider behavior needed to convey 
seriousness and gain patient cooperation. The vignettes and questions were informed 
by measurement of explicit bias in prior literature 19,20 and prior research in this setting 
21–23,19,20. Response options range from strongly disagree to strongly agree on a 4-
point scale. Summative scores are created after recoding all responses to the same 
direction; higher scores indicate higher bias towards the woman in the vignette. 
Scores range from 0 to 27.. 

  
Page 25 of 28 
Statistical Methods 
 
Number of subjects planned to be enrolled: About 80 providers will enrolled.  
 
Justification of Sample Size 
The sample size of 80 is based on feasibility: on an assumption that about 50% of providers in the selected 
facilities are likely to participate in the intervention. This sample size is powered (at 80%) to detect an effect 
size of about 0.5. The length of the pilot study might, however, be too short to see such an effect size. Thus, 
the pilot is likely not powered for formal significance testing. The effect size estimates from the pilot will be 
used to inform the sample size estimation for the R01 trial. 
Description of the study 
 
Characteristics of study facilities 
We will provide a summary table showing the capacity of each maternity unit in terms of beds, number of 
deliveries per year, and number of providers for in different cadres: doctors, clinical officers, 
nurses/midwives, and other staff. 
 
Table 1: Characteristics of Study facilities 
Facility 
Maternity 
Bed 
capacity 
Deliveries 
Per year 
Number Providers in Maternity Unit 
Doctors 
Clinical 
Officers 
Nurses/ 
midwives 
Support staff 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Baseline characteristic of the study participants 
We will provide a summary of participants characteristics by study group. The summary will be presented 
in Table 2 
 
Table 2: Baseline characteristic of the study participants  
Characteristics 
Category 
Control 
(N=40) 
Intervention 
(N=40) 
P value 
Frequency (%) 
Frequency (%) 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

  
Page 26 of 28 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 Analysis of primary outcomes at baseline and endline for control and intervention groups 
We will generate descriptive statistics (both mean with standard deviation, and median with the range) to 
understand the distribution of each of the outcome variable at both baseline and end line for control or 
intervention groups. We will use a Wilcoxon paired signed-rank test to determine whether changes in the 
outcome measures are different between baseline and endline for control or intervention group. These 
results will be presented in a Table 3. 
 
Table 3: Estimates of primary outcomes at baseline and endline   
Outcome 
measure 
Statistic 
Overall estimates 
P 
Baseline 
End line 
Control Group (N=40) 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Intervention Group (N=40) 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
We will then use a difference in differences approach to assess intervention effects: For a given outcome, 
the intervention effect is the outcome change from baseline to end line for the intervention group (T2i -T1i) 
minus that for the control group (T2c - T1c). 
Analysis of secondary outcomes at baseline and endline for control and intervention groups 
We will adopt a similar analysis plan like for the primary outcomes in sections 5.3 and 5.4. Briefly, we will 
generate descriptive statistics to understand the distribution of these outcomes at both baseline and end 
line for control and intervention groups, and we will use a Wilcoxon paired signed-rank test to determine 
whether changes in the outcome measures are different between baseline and endline for the control or 
intervention groups. We will finally use a difference in differences approach to assess intervention effects. 

  
Page 27 of 28 
References 
1 
1. 
Broman-Fulks J, Kelso K. Stress Management. In: Wellness Literacy. 2nd ed. 
2 
Kendall/Hunt Publishing Co ,U.S.; 2012. 
3 
2. 
Surinder S, Meyer C. quiz: test your knowledge oF unconscious bias. Published 
4 
online 2018. https://fsae.memberclicks.net/assets/DEI/Quiz-Unconcsious%20Bias.pdf 
5 
3. 
Cohen S, Kamarck T, Mermelstein R. A global measure of perceived stress. J 
6 
Health Soc Behav. 1983;24(4):385-396. 
7 
4. 
Afulani PA, Ongeri L, Kinyua J, Temmerman M, Mendes WB, Weiss SJ. 
8 
Psychological and physiological stress and burnout among maternity providers in a rural 
9 
county in Kenya: individual and situational predictors. BMC Public Health. 
10 
2021;21(1):453. doi:10.1186/s12889-021-10453-0 
11 
5. 
Nutor JJ, Aborigo RA, Okiring J, et al. Individual and situational predictors of 
12 
psychological and physiological stress and burnout among maternity providers in 
13 
Northern Ghana. PLOS ONE. 2022;17(12):e0278457. doi:10.1371/journal.pone.0278457 
14 
6. 
Afulani PA, Nutor JJ, Agbadi P, et al. Job satisfaction among healthcare workers 
15 
in Ghana and Kenya during the COVID-19 pandemic: Role of perceived preparedness, 
16 
stress, and burnout. PLOS Global Public Health. 2021;1(10):e0000022. 
17 
doi:10.1371/journal.pgph.0000022 
18 
7. 
Manzar MD, Salahuddin M, Peter S, et al. Psychometric properties of the 
19 
perceived stress scale in Ethiopian university students. BMC Public Health. 
20 
2019;19(1):41. doi:10.1186/s12889-018-6310-z 
21 
8. 
Shirom A, Melamed S. A comparison of the construct validity of two burnout 
22 
measures in two groups of professionals. International Journal of Stress Management. 
23 
2006;13(2):176-200. doi:10.1037/1072-5245.13.2.176 
24 
9. 
Gerber M, Colledge F, Mücke M, Schilling R, Brand S, Ludyga S. Psychometric 
25 
properties of the Shirom-Melamed Burnout Measure (SMBM) among adolescents: 
26 
results from three cross-sectional studies. BMC Psychiatry. 2018;18(1):266. 
27 
doi:10.1186/s12888-018-1841-5 
28 
10. 
Lundgren-Nilsson Å, Jonsdottir IH, Pallant J, Ahlborg G. Internal construct validity 
29 
of the Shirom-Melamed Burnout Questionnaire (SMBQ). BMC Public Health. 
30 
2012;12(1):1. doi:10.1186/1471-2458-12-1 
31 
11. 
Russell E, Koren G, Rieder M, Van Uum S. Hair cortisol as a biological marker of 
32 
chronic stress: Current status, future directions and unanswered questions. 
33 
Psychoneuroendocrinology. 2012;37(5):589-601. doi:10.1016/j.psyneuen.2011.09.009 
34 
12. 
Kirschbaum C, Tietze A, Skoluda N, Dettenborn L. Hair as a retrospective 
35 
calendar of cortisol production—Increased cortisol incorporation into hair in the third 
36 
trimester of pregnancy. Psychoneuroendocrinology. 2009;34(1):32-37. 
37 
doi:10.1016/j.psyneuen.2008.08.024 
38 

  
Page 28 of 28 
13. 
Kim HG, Cheon EJ, Bai DS, Lee YH, Koo BH. Stress and Heart Rate Variability: 
39 
A Meta-Analysis and Review of the Literature. Psychiatry Investig. 2018;15(3):235-245. 
40 
doi:10.30773/pi.2017.08.17 
41 
14. 
Järvelin-Pasanen S, Sinikallio S, Tarvainen MP. Heart rate variability and 
42 
occupational stress-systematic review. Ind Health. 2018;56(6):500-511. 
43 
doi:10.2486/indhealth.2017-0190 
44 
15. 
Perrotta AS, Jeklin AT, Hives BA, Meanwell LE, Warburton DER. Validity of the 
45 
Elite HRV Smartphone Application for Examining Heart Rate Variability in a Field-Based 
46 
Setting. J Strength Cond Res. 2017;31(8):2296-2302. 
47 
doi:10.1519/JSC.0000000000001841 
48 
16. 
Lennartsson AK, Jonsdottir I, Sjörs A. Low heart rate variability in patients with 
49 
clinical burnout. International Journal of Psychophysiology. 2016;110:171-178. 
50 
doi:10.1016/j.ijpsycho.2016.08.005 
51 
17. 
Afulani PA, Ogolla BA, Oboke EN, et al. Understanding disparities in person-
52 
centred maternity care: the potential role of provider implicit and explicit bias. Health 
53 
Policy and Planning. 2021;(czaa190). doi:10.1093/heapol/czaa190 
54 
18. 
Afulani PA, Okiring J, Aborigo RA, et al. Provider implicit and explicit bias in 
55 
person-centered maternity care: a cross-sectional study with maternity providers in 
56 
Northern Ghana. BMC Health Services Research. 2023;23(1):254. doi:10.1186/s12913-
57 
023-09261-6 
58 
19. 
Haider AH, Schneider EB, Sriram N, et al. Unconscious Race and Class Biases 
59 
among Registered Nurses: Vignette-Based Study Using Implicit Association Testing. J 
60 
Am Coll Surg. 2015;220(6):1077-1086.e3. doi:10.1016/j.jamcollsurg.2015.01.065 
61 
20. 
Green AR, Carney DR, Pallin DJ, et al. Implicit bias among physicians and its 
62 
prediction of thrombolysis decisions for black and white patients. J Gen Intern Med. 
63 
2007;22(9):1231-1238. doi:10.1007/s11606-007-0258-5 
64 
21. 
Afulani PA, Buback L, Kelly AM, Kirumbi L, Cohen CR, Lyndon A. Providers’ 
65 
perceptions of communication and women’s autonomy during childbirth: a mixed 
66 
methods study in Kenya. Reproductive Health. 2020;17(1):85. doi:10.1186/s12978-020-
67 
0909-0 
68 
22. 
Afulani PA, Kelly AM, Buback L, Asunka J, Kirumbi L, Lyndon A. Providers’ 
69 
perceptions of disrespect and abuse during childbirth: a mixed-methods study in Kenya. 
70 
Health Policy and Planning. 2020;(czaa009). doi:10.1093/heapol/czaa009 
71 
23. 
Buback L, Kinyua J, Akinyi B, Walker D, Afulani PA. Provider perceptions of lack 
72 
of supportive care during childbirth: A mixed methods study in Kenya. Health Care for 
73 
Women International. 2021;0(0):1-22. doi:10.1080/07399332.2021.1961776 
74 
 
75 
 
76 
 
77 
 
78