dataesr/scientific-paragraphs-categorization huggingface dataset:
Cost Effectiveness A cost-effectiveness analysis will determine if the implementation of INFANT represents 'value for money' when measured incrementally against usual practice. A 'trial-based evaluation' (i. e. , costs/outcomes exactly as per the trial) will be conducted using primary and secondary outcomes. A 'modelled economic evaluation' will also be conducted, using the difference in BMIz between the intervention and control arms, and extending the target population, time horizon and decision context. A cohort multi-state lifetable model (53) will be used to translate changes in infants' BMIz to long-term health outcomes (healthy adjusted life years). Simulation-modelling using the @RISK software package will calculate 95% uncertainty intervals around epidemiological inputs and cost estimates.
Governance Practice and policy partners engaged in this proposal are key to the implementation, and importantly the sustained delivery of INFANT within Victorian local government areas. An Implementation Advisory Group consisting of Department of Health representatives (including MCH services), Municipal Association of Victoria representatives (responsible for delivery of MCH services), representatives of local health services who have successfully implemented INFANT for a number of years and the research team meet bi monthly. This group has input into all aspects of this proposal including recruitment and promotion approaches, development and delivery of INFANT implementation training, data collection and study measures. A larger Partnership Advisory committee has also been formed (consisting of all 8 practice and policy partners) meeting annually.
FUNDING This research is funded by The  National Health and Medical Research Council  (Grant number:  GNT1161223 ), the  Victorian Health Promotion Foundation , and the  Victorian Department of Health. KH is supported by an  Australian Research Council   Future Fellowship  ( FT130100637 ). VB is supported by an  Alfred Deakin Postdoctoral Research Fellowship. Beyond the peer review process, the funding bodies do not have a role in study design; data management, analysis, nor interpretation; writing of the report; or final authority over the decision to submit study findings for publication.
Background Medication errors can occur at any step in the medication process in hospitals (prescribing, dispensing, and administration). Between 1999 and 2005, errors most often originated during the administration phase of medication use (33%) in the American MEDMARX selfreported database  [1]. Administration error is defined as a deviation from the prescriber's medication order as written on the patient's chart  [2]  and concerns the nurse administering the medication to the patient. Reported administration error rates are approximately 10% using the observation technique  [3, 4] , which has been found to be the best available method for determining the prevalence of administration errors. Error-provoking conditions that influence administration errors include organizational factors (high workload or the number of patients under the care of the nurse), the nurse (inexperience of the nurse or inadequate training), patient factors (polymedication), and working environment (noisy, distractions, and interruptions  [5] [6] [7] [8] [9] [10] [11] )  [12, 13].
Randomization and random drawing Units were randomized at the end of the preintervention period into either the experimental or control group (standard of care) by the European Georges-Pompidou Hospital Clinical Trial Unit. Randomization lists were stratified by center and medical field (critical care, surgery, and medical field), with a 1:1 allocation, and created using the random selection function of Excel, such that a similar number of units and fields were enrolled in each group. The Additional file 1 shows the distribution of randomized units per hospital. During an administration round, only one nurse was observed. It was thus decided to divide the units into two or four areas, corresponding to a single nurse in charge of defined patients. Each day, the area was randomly drawn for observation by the Clinical Trial Unit, independently of the observers and nurses, using the random selection function of SAS® (SAS Institute, Cary, NC, USA).
Procedure for data collection Observation technique and training Data were collected by direct non-disguised observation  [28]. The observer watched the selected nurses from medication preparation to administration to the patient. Observation was carried out by pharmacists (n = 6) and members of the medication process group, including head nurses (n = 13), head pharmacy technicians (n = 2), and nursing managers (n = 2). All observers came from the Paris West Hospitals Group. They received training by senior pharmacists (SB, LL). The training was evaluated using a simulated scenario during one group session. The training inter-rater concordance of observers was 96. 16%.
Collected data Observations were conducted using a paper-based structured observational tool. The following data were collected: characteristics of the nurse (age, sex, and years of experience), nurse workload (number of patients under the care of each nurse), time for the round, and whether the vest was removed at the end of the round in the intervention units (intervention period). Observers recorded information concerning the drug prepared and administered: name, dose, dosage form, administration route, preparation of injectable drugs (diluent, solvent, speed of administration), number of interruptions and their type, and whether the vest was worn in the experimental groups. During the two observational periods, observers were followed during one round by one of the two senior pharmacists (SB, LL).
Odds ratios were determined with their associated 95% confidence intervals and p-values. The risk factors identified in the literature were studied in univariate analysis: patient (age, which could lead to polymedication), nurse (experience), organizational factors (number of patients under care during each round, number of OE per administration round), and working environment (unit configuration named "unit exposition", number of OE with at least one task interruption). For the unit configuration, an open area of the unit was considered to be more exposed to interruption. A descriptive analysis was performed for the potential clinical impact of errors and the satisfaction survey. Analyses were performed using the SAS (version 9. 4, SAS Institute, Cary, North Carolina) and R (version 4. 0) softwares.
Ethics The protocol was approved by institutional review boards and ethics committees (CPP Ile-de-France N°2016-A00211-50) and registered at ClinicalTrials. gov (NCT03062852, date of first registration: 23/02/2017). The study was also presented to the medical committee, the patient commission, the commission for Health, Safety and Working Conditions, and during nurse and physician information sessions in the units. Head nurse staff members collected the written consent of the nurses. Nurses were informed that the study was a direct observation study of medication administration and preparation tasks to study the impact of a 'Do not interrupt' vest on administration errors and interruptions. All participating nurses underwent a process of nonopposition and were made aware that participation was strictly voluntary.
There was a risk of the Hawthorne effect due to the use of the observation technique, but it was limited by the cluster-controlled randomized design, with two periods of data collection and continuity of the observers (nurses got used to the same observer for all observations). The preintervention period was performed to evaluate the improvement of nurses' practices without the effect of the intervention. Concerning the statistical analysis, specific attention was given to compliance in wearing of the vest to ensure powered intention-to-treat analysis. Our study also had several limitations. We excluded one unit due to missing data for the intervention period. The intervention consisted of the vest wearing and the posters in the units but nurses were not instructed to respond in a certain manner when they were interrupted to limit the effect of the interruption  [42].
Acknowledgments The authors wish to thank  Agriopale  and more specifically  Camille Dusannier  for providing the compost samples. Special thanks to  Arnaud Lefebvre  and  Julien Dassonneville from LGCgE  for their contributions to the setting up of the ex-situ experiment. We also thank gardeners for allowing us to collect soil samples in their gardens and cities of Nantes and Lille for giving us access to the community gardens.
Context and Design HEALTH BACKGROUND Despite being one of the more wealthy countries in the Central Africa region, and the country's relatively high health spending of $59 per capita in 2014 (World Health Organization Global Health Expenditure database, 2016), Cameroon's health indicators resemble countries that spend much less on health care  (The World Bank, 2016). Cameroon did not achieve Millennium Development Goals 4 & 5 which called for large reductions in maternal and child mortality. Moreover, Cameroon is one of the few countries with high maternal mortality where maternal mortality did not decrease at all between 1990 and 2015  (Alkema et al. , 2015). One in every 25 women of reproductive age in Cameroon continues to die from pregnancy-related causes (Institut National de la Statistique (INS) et  ICF International, 2012).
STATISTICAL METHODS FACILITY AND CATCHMENT AREA EXCLUSIONS Most of the analysis included in this report includes only those health facilities that were surveyed at both baseline and endline. Similarly, for all of the results coming from the household surveys, these results include only those villages that were surveyed at baseline and endline. The only exceptions are the analyses that use data from the direct observation and exit interviews from ANC and child health consultations. These analyses include all of the women and children who received these services on the day of the facility survey. The reason that we deviated from the above stated exclusion criteria is that, especially in the ANC sample, the exclusion results in large sample size loss. Specifically, the full sample for ANC included data from 733 visits and 118 health facilities while the restricted sample included 561 visits at 46 health facilities.
At endline, the health facility survey collected data on the amount of funding available at the health facilities for operation expenses, including the amount of revenue collected from cost recovery, funding provided directly by the Ministry of Health, and subsidies from the PBF program. To verify the equal receipt of financing, we compared data on total financing available, and subsidy amount received between T1 and C1 health facilities. The first panel of table 3 displays the average amount of health facility revenue from all of these sources during each trimester of 2014 in T1, the full PBF group, and C1, the increased financing group. The second panel compares the amount of subsidies received in each of the two groups. Neither the subsidy amount, nor the total amount of financing available at the health facility differed statistically between the two groups during any trimester of 2014.
We also sought to assess whether per capita payments were equal between groups, as this was the intention of the study. However, exact catchment area population numbers are not known. Therefore, we standardized payments by the number of health workers in each facility as a proxy measure for catchment area population. Using this proxy measure, we found no differences in payment amounts between groups (Table  3 ). We also confirmed the subsidy data collected in the facility questionnaire by requesting subsidy data from the regional funds. These data, which represent the payments made by the regional funds to the health facilities in treatment groups T1 (full PBF) and C1 (additional financing), are presented in Appendix Figures  A5 -A7. These figures also show that the total payments provided to the health facilities in each treatment group were equal during the entire study period.
FACILITY SURVEY UTILIZATION RESULTS This section describes the results of PBF on health services provision as recorded in facility registers. To assess the reliability of these data, we examined the health service counterverification data that were collected routinely as part of the PBF program design through community client satisfaction surveys. Health service verification took place in all PBF health facilities, as well as in health facilities in control groups C1 (additional financing) and C2 (additional supervision). Thirty-five patients were sampled for 7 health service categories each quarter. Figure  2  shows the percentage of patients who were reported by health facilities to the PBF verification terms, who were later confirmed to have received health services at the health facility.
Facility level provision of health services in the six months before the survey took place was assessed using patient registers from study health facilities. All data were collected at the monthly level; therefore, the interaction term coefficients represent differences between groups in the change in monthly services provided. Provision of skilled delivery did not increase in the control group during the study period. Relative to the control there were no statistical differences in mean monthly provision of skilled delivery in the full PBF and the additional supervision group; however, there was a relative increase of approximately 2 monthly deliveries in the additional financing group (additional financing 1. 855, p-value = 0. 071). The overall change between 2012 and 2015 in antenatal care provision in study health facilities was positive, but was not statistically significant.
Consequently, the children included in the household data would have received vaccinations prior to the months covered in the facility level data at endline (i. e. before the vaccine stock-out). Both sets of results show that the PBF group, and to a lesser degree, the additional financing group, performed much better than the control group. Therefore, the findings from the household and facility data showing large and statistically significant differences between groups are consistent, and we believe that the level changes can be explained by a country level shock affecting vaccine availability that affected the facility, but not the household data. Facility-level provision of the third dose of polio vaccine decreased by approximately five vaccines per month in the control group (Table  4 , column 6).
ANTHROPOMETRICS The height and weight of all children under 5 years of age were recorded during both baseline and endline surveys. Though nutrition indicators were not incentivized in the Cameroon PBF program until 2015, we used the height and weight data collected in the household survey to assess the effect of PBF on child nutrition outcomes. Using the WHO child growth standard's Stata package, we calculated a z-score for child height-for-age, weight-for-age, and weight-for-height. These zscores represent the location where each child falls according to international standards defining healthy child development. Using these scores, we calculated the prevalence of stunting (heightfor-age of less than -2 standard deviations from the mean), underweight (weight-for-age of less than -2 standard deviations from the mean) and wasting (weight-for-age of less than -3 standard deviations from the mean).
These statements were taken from the World Health Organization's (WHO) Well-being Index (Appendix Section WHO Being Index) and included, for example, "In the last two weeks, I have felt active and vigorous. " For each question, health workers were asked to indicate whether the statement described their state most of the time, more than half the time, less than half the time, only rarely, or never. For the purposes of analysis, these data were recoded into binary indicators. Responses were coded as "0" if the health worker replied half the time or less, and "1" otherwise. Thus, point estimates on the postXintervention arms indicate the percentage point increase or decrease in probability that a respondent in a given intervention group reported that a given statement was true at least half the time, relative to the pure control, after adjusting for individual-level indicators (age, sex, marital status, and education level) and facility-level indicators (type of health facility, urban/rural status).
HEALTH WORKER AVAILABILITY IN THE HEALTH FACILITY The facility survey asked the head of the health facility, or the most informed staff member, to list the names of the all of the health workers employed at the health facility. This information was recorded onto the staff roster. The staff roster also collected information on the post occupied by each health worker and on whether they were present on the day of data collection. Table  18 , column 1 presents results from analysis of the number of nurses present at the health facility on the day of data collection. There was a small and non-significant increase in the number of nurses present over the study period. The increase in the number of nurses in the PBF group was greater than in the full control group (p-value = 0. 01). Adding the coefficient on the interaction term of PBF and post to the coefficient on the post indicator (0.
THE QUALITY OF ANTENATAL CARE Enumerators observed 729 ANC consultations. They compared each exchange against a standardized checklist and noted whether the health worker performed the following eleven routine activities: took a background, 3  asked about past issues, 4  asked about current issues, 5  provided iron supplementation, gave advice about warning signs, 6  helped to prepare for the birth, 7  checked HIV status, tested for syphilis, provided malaria prophylaxis, discussed appropriate nutrition, and checked the following vital signs: blood pressure, weight, conjunctiva, hemoglobin, rhesus, urine glucose, uterine size, fetal heartbeat and fetal presentation. As with the child health consultations, these data were used to calculate aggregate quality scores. National protocols provide guidelines for care that are specific to the number of prior visits and gestational age of the pregnant women.
In both rounds, the primary inclusion criteria for the household survey was that the household must have contained at least one woman who had been pregnant in the 24 months preceding the survey. Though the sample was meant to be a panel, with repeat sampling of the same households at baseline and endline, only a small proportion of households sampled at endline were also sampled at baseline. For instance, only 29% of the 4,813 households from which the 6,275 pregnant women surveyed at endline resided were also sampled at baseline. Therefore, the surveys are analyzed as repeated cross-sectional surveys rather than panel data in this report. The household survey was administered to all members of the household who were present on the day of data collection. Demographic data including educational attainment and labor force participation was collected from all adult members of the household.
Data on recent illness and health care use in the past four weeks were collected from all household members, with primary care givers providing information about child health. Household level data on housing characteristics, household assets and household level income were provided by the head of household. Additionally, the household survey contained separate modules for women of reproductive age (15 -49 years), women who had been pregnant in the 24 months before the survey, and for children under five years of age. The main health themes covered in these modules included:  Health behaviors for MCH services  Health seeking behaviors, barriers to use and health service use  Household health expenditures  General perceptions of health service quality In addition, the survey teams weighed and measured the height of all children aged under 5 years present in the household during the survey team's visit.
The World Bank. (2016). World Development Indicators. from The World Bank The World Bank. (2017). Cameroon Public Expenditure Review. United Nations Statistical Division. (2015). Millennium Development Indicators: Country and Regional Progress Snapshots. from http://mdgs. un. org/unsd/mdg/Resources/Static/Products/Progress2015/Snapshots/CMR. pdf World Health Organization Global Health Expenditure database. (2016). Health expenditure per capita (current US$). (December 8). http://data. worldbank. org/indicator/SH. XPD. PCAP Zeng, W. , Cros, M. , Wright, K. D. , & Shepard, D. S. (2013). Impact of performance-based financing on primary health care services in Haiti.
In T. W. Bank (Ed. ). Washington DC: The World Bank. The World Bank. (2016). World Development Indicators. from The World Bank The World Bank. (2017). Cameroon Public Expenditure Review. United Nations Statistical Division. (2015). Millennium Development Indicators: Country and Regional Progress Snapshots. from http://mdgs. un. org/unsd/mdg/Resources/Static/Products/Progress2015/Snapshots/CMR. pdf World Health Organization Global Health Expenditure database. (2016). Health expenditure per capita (current US$). (December 8). http://data. worldbank. org/indicator/SH. XPD. PCAP Zeng, W. , Cros, M. , Wright, K. D. , & Shepard, D. S. (2013). Impact of performance-based financing on primary health care services in Haiti. Health Policy Plan, 28(6), 596-605.
Programme leadership suggests that the selection criteria for CDSs emphasise both clinical expertise as well as 'social skills,' which are needed to overcome clinician resistance. Additionally, during the time period of the study, the CDSs were overwhelmingly female with only one male nurse, which is consistent with the gendered nature of the nursing profession in general. Procedurally, CDSs review clinical notes and check these notes for opportunities to 'accurately assign a code' (https://acdis. org/articles/2019-update-guidelinesachieving-compliant-query-practice) and ensure that the clinical documentation supports the use of the code. National guidance supports varied reasons for querying. While the reason for a query may be multifactorial, it serves to support the overarching requirements set forth for the reporting of diagnoses under the Uniform Hospital Data Discharge Set.
In the context of the dependent variable, it is important to note that, at this organisation, response time is measured as the time taken between a query being sent by the CDS to a clinician, and the clinician altering the record with the clarifying documentation or replying to the CDS that the current documentation was accurate as is. Using this data set, we generated four additional control measures. First, we control for the patient's length of stay in the hospital at time of the query to control for variance related to the point in time in the patient care process at which the query request is sent. Next, we control for the day of the week and the month of the query to account for temporal variations in work within the hospital. For instance, new residents join the hospital every July, which can lead to delays in responses to queries.
Apparatus. e experiment was conducted in a fixedbase simulator (see Figure  2 ). e steering wheel and pedals used in the simulation were from a Sensodrive SENSO-Wheel running at 1 kHz. e simulation was developed using JOAN  [54] , an open-source software framework developed at the TU Delft that builds on the CARLA opensource simulator (Version 0. 9. 8  [55] ). e vehicle dynamics were simulated by the Unreal Engine using PhysX  [56]. e scenery was shown via a 65-inch 4k TV with a 60 Hz refresh rate. e virtual camera settings provided a 90-degree horizontal field of view. e simulation and data logging were updated at 100 Hz. e sound of the vehicle's engine was presented via Beyerdynamic DT-770 Pro 32 Ohm headphones. e car's interior and bonnet were included in the visualization to enhance the perception of road position and vehicle speed and were the same for all conditions (Figure  2 ).