dataesr/scientific-paragraphs-categorization huggingface dataset:
Methods A systematized design was used to make an overview of the knowledge on interventions focused on the health, well-being, or aging of healthcare workers. The three following electronic academic databases were searched to identify relevant articles. The string-based search was elaborated with a librarian and is available in Appendix 1. The strategy used to optimize the stringbased search focused on the following axis. We performed searches in electronic databases operated in the English language using a search strategy in April 2022 and update in October 2022. Records published in any year and any language were included. Since the aim was on overview of the knowledge on the subject and given the sufficient number of papers, only systematic reviews were included. However, the plurality of field explored and the heterogeneity of study types did not allow an umbrella review with details of extraction neither a meta-analysis  [4].
Clinical management, patients' attitude to clinical follow-up, and the correct interpretation of screening results may, however, strongly influence the potential benefit of this diagnostic procedure. Our data were collected in the context of a cross-sectional study, aimed at assessing the prevalence of FGS and HPV infection in the Boeny region of Madagascar, which was selected for its estimated S. haematobium prevalence of more than 50% in the adult population. For this purpose, we established an experimental screening program comprising a colposcopy combined with a symptom-based questionnaire for FGS at primary healthcare centers (PHCCs), allowing for the detection of patients with both FGS and HPVrelated dysplasia (HPV-dy). The eligibility criteria were as follows: being aged between 18 and 49 years and not pregnant, being able to provide informed consent, and fluency in either French or Malagasy.
Further treatment for patients with histologic confirmation of high-grade HPV-dy or CC was performed at the local hospital. Among the women who presented with lesions that were suspected to indicate both diseases, three cases were selected for reporting because of the peculiar challenges observed in the diagnostic-therapeutic flow (Supplementary Table  1 ). These cases were chosen because they reflected (i) the complexity of the clinical management of suspicious lesions in a context with limited possibility of long-term follow-up, (ii) the possible inaccuracy of a histologic diagnosis, and (iii) the potential for patient reluctance to continue the diagnostic-therapeutic pathway. Qualitative data were additionally collected from two of the three patients described in this case report (patients 1 and 3), specifically the two patients referred for additional medical services, with the aim of exploring drivers for the acceptance or refusal of medical diagnosis and consequent treatment.
Data was collected through in-depth interviews driven by a structured topic guide, by a trained local female medical doctor with extensive experience in conducting qualitative data collection. Interviews were performed in Malagasy, recorded, and translated into French and English for analysis. Ethical approval was obtained from the National Ethics Committee of Madagascar (ref. The patient (20 years old) was evaluated on 4 May 2021 in Marovay. She reported one pregnancy brought to term and complained about dyspareunia for more than 6 months prior to consultation, with regular but painful menses and no contraception in place. No chronic or recent therapy nor history of previous treatment with praziquantel was recorded. The colposcopy detected grainy and homogeneous sandy patches, consistent with FGS, together with the finding of cervical contact bleeding.
Patient 3, despite the accessibility of free health services, decided not to use them owing to the discovery of a pregnancy and fear of the possible clinical consequences of continuing the follow-up. The typical determinants for healthcare utilization can be divided into supply-sided and demand-sided pillars  (54, 55) , relating to the attitudes/behaviors toward health services of providers' and users', respectively. During our study, the availability of the screening and follow-up service temporarily mitigated the supply barriers but nevertheless did not fully overcome the demand barriers, as in the case of Patient 3. This showed how both the supply and demand pillars must be addressed to improve access to and utilization of healthcare  (55). Our qualitative data retrieved from Patients 1 and 3 highlight the importance of health literacy and health education in the decision-making process about the use of medical services.
During the scoping step (S2), these data were used to determine the scope of the HIA, i. e. , to select the determinants of health on which to focus for impact assessment. Thus, data were first used to identify the hypothetical impacts on health that the new project might have and develop the causal mechanisms and pathways that link them to the project. These hypotheses, represented in a causal model, were then confronted with the local health and social issues related to the project. The selection of health determinants to be investigated was based on the following criteria: (a) Territorial issues: does the determinant affected by the project contribute to the health status of local populations? (b) Impacts nature and magnitude: is the determinant likely to be significantly affected by the project? Or is the determinant likely to significantly affect the health of the populations? Or are the effects of the project likely to be unevenly distributed among different population groups? (c) HIA added value: can the approach provide new data and/or solutions in addition to the information already available? (d) Data availability: will it be possible to mobilise and/or produce the necessary data to estimate the potential effects of the project on the determinant concerned? Each health determinant was listed and prioritized in a scoping matrix (see Table  3 ).
). Finally, these data were analysed using the most appropriate methods and models: statistical data processing for outdoor air quality (using the WHO-promoted AirQ+ software), noise (Miedema and Oudshoorn model), and road accidents (Nilsson-Elvik or Power model); map-based approach for local development as well as mobility and access to health services; literature review for all determinants of health; NVivo qualitative analysis of the comments collected during the public inquiry on the COS project for all determinants of health, in particular local development and the living environment. The NVivo-assisted analysis of the public inquiry was conducted in two phases: an initial thematic analysis of extracts dealing directly or indirectly with the determinants of health included in the scope of the study (724 such extracts were identified), in order to try to understand how the project was likely to impact peoples' living environment and wellbeing and compare this information with the scientific literature; a second computer-assisted analysis of all the 1349 extracts in the public inquiry, to identify the links made by the contributors between the project, health determinants, and health outcomes.
In recent years, the development of clinical data warehouses (CDW) has put Electronic Health Records (EHR) data in the spotlight. More and more innovative technologies for healthcare are based on these EHR data. However, quality assessments on EHR data are fundamental to gain confidence in the performances of new technologies. The infrastructure developed to access EHR data -CDW -can affect EHR data quality but its impact is difficult to measure. We conducted a simulation on the Assistance Publique -Hôpitaux de Paris (AP-HP) infrastructure to assess how a study on breast cancer care pathways could be affected by the complexity of the data flows between the AP-HP Hospital Information System, the CDW, and the analysis platform. A model of the data flows was developed. We retraced the flows of specific data elements for a simulated cohort of 1,000 patients.
Introduction Real-world data, and especially Electronic Health Records (EHR) data, are increasingly used to develop innovative digital technologies and new services supporting various activities of health professionals, in research, care and training. To integrate data from multiple sources and provide associated services for secondary use of this data, hospitals have recently started to develop Clinical Data Warehouses (CDW)  [1]. However, EHR data are not without flaws, and caveats and recommendations have been raised when reusing them for research  [2, 3]. 1 Corresponding Author: Sonia PRIOU, Université Paris-Saclay, CentraleSupelec, Laboratoire Génie Industriel, Gif-sur-Yvette, France; Email: sonia. priou@centralesupelec. fr A major aspect of re-using real-world data is data quality assessment.
For EHR data, it doesn't just concern the data entered in the Hospital Information System (HIS). Issues can occur and data can be lost or somehow modified during the extraction, the transformation, or the loading phases, ultimately affecting the dataset provided to researchers  [4, 5]. This could lead to a loss of power or to systematic bias, by shifting or reversing the results of analyses. Since CDWs combine data from multiple pieces of software, many of them proprietary, they ultimately resemble black boxes, and tracing back how data were processed is hard. One can manually compare the data accessible in the CDW with the data in the HIS  [6] , but this procedure is time-consuming, and impossible when CDW data are de-identified. The objective of this paper is to model and simulate the effect of the complex structure of a CDW on the data extracted from the HIS.
Modeling care pathway reconstruction We considered the following standard care pathway, common to all hospitals included in the study: first consultation of a hospital specialist, cancer diagnosis, therapeutic strategy choice, and administration of treatment. At each step of this care pathway, distinct data elements are recorded in the HIS by clinical staff. We identified the key information needed to reconstruct the standard care pathway and selected the minimal dataset of data elements to find them (figure  1 ). To identify the first consultation related to the patient's cancer diagnosis, the date and the purpose of the visit are needed. Both are available in the consultation report. The cancer diagnosis and extension status are made by pathology examination and Positron Emission Tomography (PET) imaging, respectively.
Modeling data processing Thanks to the reading of internal technical documentation and the help from CDW professionals, we modelled the structure of the CDW. For each data element of the identified minimum dataset, we modelled its trajectory: entry in the HIS, transfer in the CDW and provision on the analysis platform. The trajectory followed by the data elements comprises several steps, between which data is transferred from one element to the next. Each data transfer was qualified according to the type of transformation applied: none, data format conversion, filtering, application of NLP functions, standardization and pseudonymization. Each of these steps was associated with a probability of correct data transfer: in case of impossible transfer, this probability was set to 0; in case of an identical copy, the probability was set to 1; for all other transformations, we modelled the probability of success using a uniform distribution between 0.
CDW Datalake Extension status hospitals uses a completely different EHR software than the other 4, so none of its data is integrated into the AP-HP CDW. For that reason, we decided to focus only on the other 4 hospitals, that we will name hospitals A, B, C, and D. We distributed our 1,000 patients across these 4 hospitals and per year following empirical distributions reproducing real proportions in the context of AP-HP. We hypothesised that patients stay in the same hospital during their care and that their entire care pathway occurs during the same year. We used Monte-Carlo simulation on the success rate of data flows and simulated the success rate coefficients 10,000 times. Each time, we replayed the data trajectory to measure which data elements were available in the analysis platform.
We measured the median and inter-quartile range of the percentage of patients for whom all the data elements necessary to rebuild the care pathway were available in two scenarios: x the "best case" scenario, where all missing elements are concentrated on the same patients (i. e. , the same subset of patients are missing their laboratory results, consultation reports, imaging reports…), x the "random distribution" scenario, where data losses per data elements are randomly assigned to patients (i. e. , missing laboratory results are attributed to a random subset of patients; missing consultation reports are attributed to another random subset on patients, possibly intersecting with the previous one).
Trial design From November 2021 to April 2022, we implemented a parallel-arm, multicentre RCT with individual randomisation, comparing the impact assessed by a pre-post questionnaire of MI intervention versus standard care (educational leaflet about vaccination) as the control (ClinicalTrials. gov: MOTIVAC-MATER-Confiance, number NCT05093452). This article presents the results of the comparisons just after the MI or leaflet distribution; further results of the RCT covering a 7-month follow-up will follow this initial report.
Data collection and outcomes We collected data through two self-administered questionnaires, one before the MI or leaflet distribution (T0, pre-questionnaire, 34 items) and a second afterwards (T1, post-questionnaire, 35 items). They were completed by study participants during the post-partum stay at the maternity ward; 80% of the participants completed both on the same day. Both questionnaires can be found in the Supplementary Material S9. The T0 questionnaire collected respondents' demographic and socioeconomic characteristics, which included age (continuous), living with a partner (yes/no), birth rank of the newborn (continuous), education level (high school or below/at least some post-secondary education), and perceived financial situation (level of security in six categories: cannot manage without debts, cannot manage easily, have to be careful, only fair, OK, comfortable).
Education level and perceived financial situation (the latter was grouped into two categories for the analyses: cannot manage without debts or easily ('insecure') vs the other four categories ('not insecure')) were used each individually as proxies for social deprivation level. The survey also collected mothers' own vaccination status against influenza during pregnancy (yes/no). Using the T0 and T1 questionnaires, we collected data related to the two primary outcomes: (i) VH assessed with a modified version of the Parents' Attitudes about Childhood Vaccines (PACV)  [32]  validated questionnaire (13 items, see Supplementary Material S1 for PACV adaptation) used in the PromoVac and PromoVaQ studies  [15, [18] [19] [20]  and (ii) VI (i. e. intention to vaccinate their infant) at 2 months assessed with the following item, also used in those studies: How sure are you that you will vaccinate your baby at 2 months of age? (on a scale from 1 = 'not certain at all' to 10 = 'absolutely sure').
Analyses were performed by intention-totreat (ITT): all patients who agreed to participate and completed the pre-questionnaire (T0) were included, with missing post-questionnaire (T1) data handled by the mixed model without imputation  [35]. Per protocol (PP) sensitivity analyses were conducted by excluding patients who did not respond to the post-intervention questionnaire. Analyses used SAS 9. 4 (SAS Institute Inc. ), with statistical significance set at 0. 05.
Acknowledgements All authors thank the maternity wards of  Saint-Joseph  and  Sainte-Musse  and especially the three midwives who conducted the motivational interviews and led the study,  Bilel Mebarki , who designed all the supporting documents for the project (guide for midwives, brochures for parents, questionnaires), the  ORS PACA  field team ( Gwenaelle Maradan ,  Cyril Bérenger ,  Cheraz Riabi ,  Fernanda Felix Da Costa ), which supervised the smooth running of the study in the maternity units and data collection and entry and  Jo Ann Cahn  who reviewed the manuscript's English.
Step 1: Problem analysis This first step of IM is to fully describe the health problem, assess the needs (qualitative or quantitative modalities) of stakeholders, and identify the determinants of the behaviors to be modified individually in the targeted population. In the IM protocol, these determinants are combined with key concepts from social and health psychology (e. g. knowledge, skills, selfefficacy, outcome expectations, perceived social norms) to identify the best theories to explain the problem. The relevant literature on OSA was systematically reviewed in the PubMed database to identify the studies exploring patient expectations about PA behavior change and the perceived facilitators and barriers for PA engagement. The titles, keywords, and abstracts of papers were screened with terms referring to (1) barrier or facilitator, (2) obstructive sleep apnea, and (3) physical activity.
Limitations The study has several limitations. First, the effectiveness of the intervention has not yet been evaluated by an RCT. Second, PubMed was the only database used in the systematic literature review. Although it is one of the largest available databases, we acknowledge that some useful interventions may have been overlooked. Third, due to time constraints, no focus group was organized during the problem analysis step. Indeed, other researchers who have used the IM protocol have described this as very time consuming, 138-140 and the time schedule of the project prevented us from carrying out a focus group. However, the development of this mHealth intervention was based on previous research carried out by our team in a population of obese patients, many of whom had OSA. 98 This first experience allowed us to identify levers and understand the needs of these patients regarding the different aspects of a telerehabilitation program.
METHODS This study is a non-randomized observational programmes evaluation with two time points -pre-and post-intervention. The study consisted of secondary analysis of de-identified data collected via routine monitoring and evaluation activities by ACF for their ESMI programs in 2020. Given the use of only de-identified secondary programmatic data, the Johns Hopkins Institutional Review Board issued a "Not Human Subjects Research" determination for this study in accordance with the Department of Health and Human Services regulation 45 CFR 46. 102  [17]. Meetings with each of the ACF MHPSS country teams were held to further understand the implementation of ESMI in their respective context.
Study population and design Adults and youth (12-17 years old) who participated in ESMI were living in the major cities of of Liberia, Ivory Coast, and Sierra Leone. In each country, ACF targeted areas with vulnerable communities experiencing a relatively high prevalence of COVID-19 within the existing geographical coverage of ongoing ACF programmes. ESMI was also implemented in Madagascar in 2020, but due to the high percentage of loss to follow-up, the data set was removed from this study. There were some children (7-11 years old) who received ESMI in Ivory Coast and Sierra Leone but due to the small number, their data were also removed from the final data analyses.
Data analyses All analyses were performed separately for each country's data set using Stata/BE 17. 0  [20]. Exploratory data analyses were performed for the variables of interest including, psychological distress, social support, symptoms of distress, identified problems, coping strategies, and demographic characteristics. We used Shapiro and Wilk's normality test for the continuous data in each country's data set  [21]  and pre-specified statistical significance to a P-value of <0. 05. Paired t-tests were used to determine the differences in the mean scores for emotional distress and social support at baseline and follow-up for each country. Pairwise correlations were computed for changes in distress and changes in social support. Bivariate analyses and linear regressions were performed with the primary outcome variables of change in distress and change in social support and covariates of age (continuous and categorical), sex, and household status.
Acknowledgements: We are thankful to all the persons who participated in this project from the  Programme National de Santé Universitaire et Scolaire Adolescents-Jeunes (PNSSU-SAJ)  and the  Ministry of   Health and Public Hygiene in Ivory Coast in Ivory Coast, Community Association for Psychosocial Services (CAPS)  and the  Ministry of Health and Sanitation (MOHS) in Sierra Leone , and the  Community Health Initiative (CHI)  and the  Ministry of Health in Liberia. We are grateful to all  Action Against Hunger/Action contre la Faim  staff for their continuous involvement along this project.
(2. 2) STUDY POPULATION The target population for this research was public elementary schools on the territory of the CIUSSSE-CHUS. Each participating school had to: 1) be a public elementary school, 2) be located in the CIUSSSE-CHUS territory, and 3) agree to receive PI services. Quantitative data were collected for all eligible schools. Qualitative data were gathered within three schools (n = 3 cases) chosen according to criteria selected by the advisory committee: • affiliated school board (three different school boards out of the five on the territory), • language of instruction (one English and two French schools), • socioeconomic level (at least one school considered disadvantaged [deciles 8, 9 or 10] according to the socio-economic environment index [26]), and • rurality (at least one rural and one urban school).
(2. 3) DATA COLLECTION AND ANALYSIS This research project was conducted simultaneously with the implementation of PI. The quantitative data collection was carried out using clinical administrative data compiled by Public Health professionals during their interventions, as well as a questionnaire (Request for support; see supplementary material) created by the Public Health department and completed by schools wishing to collaborate with them. When completing support requests, schools were asked to indicate whether or not they agreed to share their information with the research team. Consent was obtained for 46 of the 48 support requests (96%) received. Out of the two schools not included in the analysis, one did not reply to the e-mails sent out by the research team, and the other preferred not to participate due to a change of leadership within the school during the school year.
Descriptive analyses were performed on categorical data (frequency and percentage) and numerical data (mean and standard deviation) from the general data collection. The qualitative data were collected to describe in detail the contexts and support requests of the three cases. To do so, individual interviews were conducted, followed by focus groups. Interviews were conducted with school board managers (n = 3), school system navigators (n = 3), school principals (n = 3), and public health professionals (n = 3) involved with the targeted school. Subsequently, four focus groups were conducted: one group included school board (n = 3) and Public Health (n = 1) managers, whereas the other three groups (one per targeted school) were held with the assigned health care professional, the school principal, the school system navigator and staff members.