Investigating Psychosocial Predictors & Employment Outcomes of Enhanced IPS Intervention A Prospective Cohort Study
Conditions: Psychiatric Disorder
Sponsor: Queen Margaret University

Official title  Investigating Psychosocial Predictors & Employment Outcomes of Enhanced IPS 
Intervention A Prospective Cohort Study 
 
NCT number  NCT04083404 
 
Date   
January 1, 2015 
 
 

Full Title Investigating Psychosocial Predictors & Employment Outcomes of Enhanced IPS 
Intervention A Prospective Cohort Study 
 
Study Type 
Observational: Cohort Study 
 
Study Status Not yet recruiting 
 
Study Start Date 01/06/18 
 
Study End Date 31/12/18 
 
Educational Programme  
Professional Doctorate, Queen Margaret University 
 
Principal Investigator Susan Prior 
 
Supervisory Team 
 
Professor Kirsty Forsyth 
Dr Donald Maciver 
 
Study Description 
 
 
 
Psychosocial factors influence successful attainment of employment; however, factors are poorly 
defined and overlapping and lack consistent measurement. This study aims to apply a theoretical 
perspective to the investigation of these psychosocial factors. 
 
The model of human occupation (Kielhofner 2008) provides a dynamic systems model to 
understand psychosocial factors influence on participation in occupations. An associated 
measurement tool the worker role interview, WRI (Braveman et al., 2005) provides a valid and 
reliable measure of psychosocial factors influence on the ability to attain employment. 
 
The purpose of this cohort study, using an existing database, is to seek to describe the 
relationship between psychosocial factors (independent variables) and employment outcomes 
(dependent variables) in a population of people with complex mental health problems receiving 
evidence-based supported employment (EBSE) augmented with theory-driven occupational 
therapy interventions. The intention is to understand the predictive nature of psychosocial factors 
on the positive outcome of success in achieving paid employment and the negative outcome of 
no success in obtaining paid employment. The WRI is routinely used in the research site; an 
NHS occupational therapy led vocational rehabilitation delivering EBSE service for people with 
complex mental health problems. An existing database of initial assessments and employment 
outcomes is available for investigation. 
 
EBSE is an evidence-based approach to support people with mental health problems into 
employment. The theory-driven occupational therapy intervention is informed by MOHO 
(Kielhofner 2008). 
 
This study will investigate the relationship between psychosocial factors and employment 
outcomes in a population of people with complex mental health problems receiving evidence-
based vocational rehabilitation? This study will aim to describe the relationship between 
psychosocial factors, as measured by the worker role interview (Braveman et al., 2005) on 
employment outcomes in a population of people with complex mental health  problems receiving 
evidence-based vocational rehabilitation?  
a) To predict the positive outcome of success in achieving paid employment 
b) To predict the negative outcome of no success in achieving paid employment 
 
 
The intention is to deductively test, using logistic regression, a theory of how identified 

psychosocial variables may predict the outcome of employment goals. In contrast to correlation 
analyses (or simple regression), where we are limited to comparing a single predictor with the 
outcome (Field, 2015), multiple regression will afford the opportunity to construct models which 
simultaneously examine the effects of all predictors (Gelman and Hill, 2006). Psychosocial 
variables will be measured using the WRI (Braveman et al., 2005). 
 
The study context 
The cohort study (de Win et al., 2007, Jager et al., 2007, Siriwardena, 2012) will be undertaken 
utilising a secondary dataset (Smith et al., 2011) from an established vocational rehabilitation 
service (VRS). This service is experienced in providing intervention based on EBSE (Bond, 
Becker and Drake, 2011a) with established high fidelity to the model. The team's interventions 
are also guided by the model of human occupation, MOHO (Kielhofner, 2008) and the WRI is 
routinely completed with all patients accessing the service. 
 
The consistency of intervention will be ensured as the service is routinely appraised for fidelity 
to the EBSE by two experts, using a standardised assessment tool the EBSE fidelity scale 
(Bond, Becker and Drake, 2011b) and the team has received ongoing training and regular 
caseload supervision to integrate evidence-based occupational therapy into practice. The 
experts appraising the service are independent of both the clinical service and the proposed 
research. 
 
Secondary Data Collection 
The established VRS provides an assessment and intervention for people with complex mental 
health problems. A dataset has been routinely collected over several years which is currently 
used to evaluate the service. Using secondary data enables researchers to address high impact 
research questions with significantly less time and resources than would be required in a study 
involving a primary data collection (Doolan and Froelicher, 2009, Smith et al., 2011, Cheng and 
Phillips, 2014, Okafor et al., 2016). 
Secondary data offers access to large sample sizes, relevant measures (in this case the WRI) 
and longitudinal data that is readily available, allowing insights into trends within the phenomena 
of interest (Smith et al., 2011). Moreover, the use of data collected as part of a routine clinical 
practice provides researchers with real-life data to test new hypotheses relevant to practice 
models. 
 
To address limitations of secondary data analysis the researcher has an established relationship 
with clinicians in the VRS and was involved in the selection of routine measures now used to 
evaluate the service. In this context, the WRI was selected as an appropriate initial assessment 
and outcome measure for service evaluation, and clinicians have routinely used this 
measurement tool for each person accessing the service it is, therefore, possible to be confident 
that a consistent and standard dataset is available for analysis. Ongoing training and 
consultation have been provided by the researcher to the clinical service to ensure the data 
quality and that any nuances and glitches are highlighted and addressed quickly to minimise risk 
to reliability and validity. 
 
Data Collection 
To ensure the quality of the data and to minimise missing data while protecting identities of 
service users, the data will be managed and transferred by a trained researcher on site at both 
services. The researcher is a senior research fellow employed at Queen Margaret University 
and holds an honorary contract with NHS Lothian. Data will be robustly anonymised prior to 
transfer. Data will be transferred from NHS secure network to secure network within Queen 
Margaret University using an NHSmail, a secure email service approved by the Department of 
Health. Once data is collected, it will be managed and prepared for analysis by the researcher 
supported by a trained statistician. 
 
 

The dataset comprises  
• 
Research ID code  
• 
Gender 
• 
Age on Admission  
• 
Ethnicity 
• 
Marital Status 
• 
Living situation (alone/shared/supported accommodation)  
• 
Quintile range of Scottish Index of Multiple Deprivation  
• 
Educational attainment 
• 
Previous employment  
• 
ICD10_CODE 
• 
Worker role interview items  
o Date of assessment 
o WRI Assesses Abilities & Limitations  
o WRI Expectation of Success in Work  
o WRI Takes Responsibility 
o WRI Commitment to Work  
o WRI Work-Related Goals  
o WRI Enjoys Work 
o WRI Pursues Interests 
o WRI Appraises Work Expectations  
o WRI Influence of Other Roles  
o WRI Work Habits 
o WRI Daily Routines 
o WRI Adapts Routine to Minimize Difficulties 
o WRI Perception of Physical Work Setting  
o WRI Perception of Family and Peers 
o WRI Perception of Boss and/or Company  
o WRI Perception of Co-workers 
• 
Employment goal 
• 
Date of final goal review 
• 
Outcome of goal (successful/ unsuccessful in attaining employment) 
 
Sample 
The most widely-used suggestion for estimating sample size for multiple logistic regression is 20 
observations per variable included in the model (Harrell, 2001). At 20 observations per variable, 
and estimating that we will investigate ten variables, we would, therefore, require 200 
observations. 
Therefore, at proposal stage we intend to aim for a minimum sample size of 200, liaison with the 
VRS has provided reassurance that this is achievable. 
 
The dataset of participants who meet the inclusion criteria will be determined by the clinical 
care team within the vocational rehabilitation service. All complete data gathered from 
participants who have provided informed consent will be accessed. 
 
Inclusion criteria 
Participants who meet the inclusion criteria of the clinical service (as below) 
Individuals have self-referred to the service as motivated to participate in 
employment Individuals must be currently supported by community mental 
health teams 

Individuals must have the capacity to consent and have given consent to participation in the 
service and for information regarding their case to be shared with NHS staff. 
 
Exclusion criteria 
Lack capacity for informed consent. 
Refusal or withdrawal of informed 
consent 
Incomplete assessment data on commencement of engagement with vocational rehabilitation 
service Withdrawal from participation in service before outcome data (obtained/did not obtain 
employment being recorded) 
 
Analysis Plan 
The collected data will be subject to analysis with the use of multiple logistic regression 
(Gelman and Hill, 2006) using the R data analysis software (R Core Team, 2015). Multiple 
logistic regression will allow us to address our research questions by constructing models 
which examine the effects of psychosocial predictors on the probability of a person with 
complex mental health problems attaining (or not attaining) paid employment (McCullagh and 
Nelder, 1989). Independent variables will be examined using multiple logistic regression to 
model the likelihood of a person with complex mental health problems attaining (or not 
attaining) paid employment. Possible confounds which may influence outcomes will be 
controlled for (e.g. age, gender, educational achievement, history of employment). 
Interpretation of the analysis will be shared and discussed with clinicians who are expert in 
their field to ensure that findings are considered from both a statistical significance and clinical 
significance position (Sedgwick, 2014b). 
 
 
Focus of Study 
Schizophrenia Spectrum and Other Psychotic Disorders 
Psychiatric Rehabilitation 
Rehabilitation, Vocational 
Employment, Supported 
Psychosocial Support 
Systems 
 
Baseline Measure 
Worker Role Interview Items - Psycho-social measure of readiness for employment 
BRAVEMAN, B., ROBSON, M., VELOZO, C.A., KIELHOFNER, G., FISHER, G.S., FORSYTH, 
K., and 
KERSCHBAUM, J., 2005. Worker role interview (WRI)(version 10.0). Chicago: Model of 
Human Occupation Clearinghouse, Department of Occupational Therapy, College of 
Applied Health Sciences, University of Illinois at Chicago. 
 
Primary Outcome Measure 
Attainment or non-attainment of employment.