dataesr/scientific-paragraphs-categorization huggingface dataset:
Data collection Five research assistants speaking the six main languages of Senegal (French, Diola, Wolof, Serère, Pulaar, Soninke) collected the quantitative data using a close-ended structured questionnaire by telephone. They administered the questionnaire using tablets with the Open Data Kit (ODK) software. The final version of the questionnaire was tested during the assistants' training and then validated by the team members after several corrections. In addition to socio-economic variables (age, gender, education, marital status, assets), we conceptualized the collected variables according to the seven dimensions of the Sekhon et al. acceptability model (Table  1 )  [24] , whose previous relevance was verified in the context of Senegal  [22]. Each of the seven dimensions was appraised using a Likert scale ranging from one (do not agree at all) to five (strongly agree).
This research is part of the  Unissahel program  (Universal Health Coverage in Sahel: https://www. unissahel. org) funded by the  Agence Francaise de Developpement (AFD)  and another grant has been supported by the  Wallonie Bruxelles International (WBI). The opinions expressed are exclusive of the authors and do not reflect the official position of the AFD and WBI. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
The data were collected using the snowball sampling approach. At the end of the data collection duration, a total of 2814 HCWs were from 61/63 provinces in Vietnam. This subsample of 'psychological impacts of COVID-19 on Vietnamese health workers over the prolonged restricted COVID responses' involved 510 respondents who were randomly assigned to this set of survey questions. There were 503 participants who completed the survey, hence, the completion rate was 98. 6%.
We sent the questionnaire link via email to a small group of 20 participants from each region of Vietnam (Northern, Central and Southern) who worked in types of health facilities (general hospitals, specialised hospitals, central clinics and commune health stations) as the core group. After completing the survey, they were invited to introduce more acquaintances and colleagues to participate in the survey. All data were encrypted and only used for research purposes. Vietnam Young Doctors Association is an organisation with the participation of more than 82 000 doctors from 20 to 45 years old in 63/63 provinces and cities in Vietnam. 14  This target group is Vietnam's core healthcare workforce.
Statistical analysis For data management and analysis, Stata software (V. 15. 1) was used. Continuous variables were presented as mean and SD, while categorical variables were presented as frequencies with percentages. Missing data were managed using the listwise deletion method. In terms of mental health, this study used multivariate Tobit regression to identify factors associated with depression, anxiety and stress in HCWs. The predictors variable included demographic variables and occupational characteristics variables. To identify factors related to the chance of requiring mental healthcare and participating in mental support, multiple logistic regression was employed. Stepwise forward techniques were employed to create reduced models, with a significance level of p<0. 2 as the inclusion threshold.
Acknowledgements BT is the PI of the research project which is managed by  Youth Research Institute  as a ministerial-level research project implemented by  Vietnam Young Physician Association. The project was supported by  Action Aid Vietnam ,  Gia Lam   Urban Development And Investment Company Limited ,  Vingroup , and supported by  Vingroup Innovation Foundation (VINIF)  (Grant No. VINIF. 2020. COVID-19. DA03). The authors would like to acknowledge  ActionAid Vietnam ,  Youth Research Institute and Vietnam Young Physician Association  for financial support. MTN is a master student of the project.
BUSSINESS INTELLIGENCE Business Intelligence (BI), also known as "business intelligence" or "business intelligence", includes IT solutions including end-of-line reports and dashboards that provide decision support to experts and enable analytical and prospective monitoring of the activity  [1]. The idea emerged in the late 1970s with the emergence of the first information centers. A system that sends requests directly to a production server. This proved to be very dangerous for the latter. In the 1980s, the advent of relational databases and client/server separated the processing of production data from the decision engine. In the years that followed, players began to define a "business" analysis layer with the aim of obscuring the complexity of data structures. Since the 1990s and 2000s, decision support platforms have revolved around data warehouses (or data warehouses) to integrate and organize information (by extraction, transfer and integration, or ETL).
 Machine learning: This is a computer programming technique that uses statistical probabilities to give computers the ability to "learn". Machine learning and artificial intelligence are two related concepts. Therefore, we can say that it is not enough to store large amounts of data in special databases, data warehouses or big data, we must use them. This is the role of data mining, and if used correctly, it can learn from large amounts of data that statistical tools alone cannot satisfy. Let's look at the principles, methods and tools used, and concrete examples that highlight the importance of data quality.
In this context, we make the best use of the available data to see how social entrepreneurship drives local dynamics using machine learning and business intelligence. Regarding the research methods used in this study, we first collected as much data as possible on the platforms and websites of the competent authorities such as the High Commission for Planning, Regional Investment Centers and local authorities. We then used data mining techniques using R to manipulate the data and extract the results. In this section, we used a data mining tool, the confusion matrix, to work on predicting the territorial attractiveness of the Fez-Meknes region from available data on projects and social entrepreneurship. This is a post based on data available on the website of the Regional Investment Center and other information available on statistics of the Fez-Meknes region, including social entrepreneurship and local attractiveness.
In this context, we make the best use of the available data to see how social entrepreneurship drives local dynamics using machine learning and business intelligence. Regarding the research methods used in this study, we first collected as much data as possible on the platforms and websites of the competent authorities such as the High Commission for Planning, Regional Investment Centers and local authorities. We then used data mining techniques using R to manipulate the data and extract the results. In this section, we used a data mining tool, the Confusion Matrix, to predict the territorial attractiveness of the Fez-Meknes region based on available data on projects and social entrepreneurship. This is a post based on data available on the website of the Regional Investment Center and other information available on statistics of the Fez-Meknes region, including social entrepreneurship and local attractiveness.
Methods The GERIA-COVID study is a longitudinal observational study conducted in the geriatric acute care unit dedicated to COVID-19 patients in the University Hospital of Angers, France, during the first wave of the COVID-19 pandemic (ClinicalTrials. gov NCT04560608). All baseline characteristics were retrospectively collected from patients' medical records, including data related to COVID-19, vital measurements, medical history, standardized geriatric assessment, usual treatments, standardized clinical examinations and blood tests on hospital admission.
Study population Patients eligible in the GERIA-COVID study were adults aged 75 years and over admitted into the geriatric acute care unit of the University Hospital of Angers, France, during the first wave of the pandemic (between March and June 2020) who were no objection for using anonymized clinical and biological data for research purpose. The inclusion criteria for the present analysis were as follows: (i) COVID-19 diagnosed with RT-PCR and/or chest-CT scan; (ii) data available on the vital status 1 year after the diagnosis of COVID-19. Ninety-seven patients were diagnosed with COVID-19 during the study period in the unit, and were recruited in the GERIA-COVID study. This analysis included all of them.
Acknowledgments The authors wish to thank the  GERIA-COVID study group. GERIA-COVID  study group: Cedric Annweiler 1 ,  Marine Asfar  1 ,  Melinda Beaudenon  1 ,  Jean Barre  ´1,  Antoine Brangier  1 ,  Mathieu Corvaisier  1 ,  Guillaume Duval  1 ,  Jennifer Gautier  1 ,  Mialy Guenet  1 ,  Jocelyne Loison  1 , Frederic Noublanche 1 ,  Marie Otekpo  1 ,  Helène Rivière  1 ,  Guillaume Sacco  1 ,  Romain Simon  1. Affiliations: 1:  Department of Geriatric Medicine, University Hospital, Angers, France. The authors have listed everyone who contributed significantly to the work in the Acknowledgments section. Permission has been obtained from all persons named in the Acknowledgments section.
|T (φ)| < 1, provided that γ 0 is sufficiently large. Figure  1  shows the map T for some suitably chosen parameters: • γ 0 = 15. 969; this value is taken from the empirical analysis in  [31] , Section 7. 2 (where it is denoted simply by γ). It corresponds to the maximum value of the leverage computed over a 4,389 time series of US Commercial Banks and Saving and Loans Associations; see  [31] , Section 7. 1, for a detailed description of the dataset. • α = 1. 64; it corresponds to a VaR constraint of 5% in case of a Gaussian distribution for the returns. • Σ = 2. 7 × 10 -5 ; this value is taken from the numerical analysis in  [32] , Table  1 , and corresponds to the exogenous idiosyncratic volatility at the time scale of portfolio decisions. • The values for ω and c are free parameters and are randomly sampled (e.
Background The use of national medico-administrative databases for epidemiological studies has increased in the last decades. In France, the Healthcare Expenditures and Conditions Mapping (HECM) algorithm has been developed to analyse and monitor the morbidity and economic burden of 58 diseases. We aimed to assess the performance of the HECM in identifying different conditions in patients with end-stage kidney disease (ESKD) using data from the REIN registry (the French National Registry for patients with ESKD).
Methods We included all patients over 18 years of age who started renal replacement therapy in France in 2018. Five conditions with a similar definition in both databases were included (ESKD, diabetes, human immunodeficiency virus [HIV], coronary insufficiency, and cancer). The performance of each SNDS algorithm was assessed using sensitivity, specificity, positive predictive values (PPVs), negative predictive values (NPVs), and Cohen's kappa coefficient.
Results In total 5,971 patients were included. Among them, 81% were identified as having ESKD in both databases. Diabetes was the condition with the best performance, with a sensitivity, specificity, PPV, NPV, and Kappa coefficient all over 80%. Cancer had the lowest level of agreement with a Kappa coefficient of 51% and a high specificity and high NPV (94% and 95%). The conditions for which the definition in the HECM included disease-specific medications performed better in our study.
Background The use of national medico-administrative databases for epidemiological studies has increased in the last two decades as an alternative to traditional observational studies. These databases were conceived to survey healthcare systems from a financial and administrative point of view, with information such as reimbursement claims, healthcare services, medical procedures, daily compensation, etc. [1]. The use of such databases for research has the potential to reduce the risk of selection bias often present in epidemiological surveys, as they are almost exhaustive. In addition, it is less costly, as the data is collected systematically and relatively easily accessible, simultaneously eliminating recall bias, as it relies on data collected systematically and not based on patient reporting with potential recollection mistakes.
National databases are helpful for longitudinal studies, as they make it possible to include extended follow-up times and large sample sizes, as well as rare events and epidemiological surveillance or surveys. Such databases are, however, not exempt from information bias  [2, 3]  as the information tends to be essentially administrative. For example, pharmaceutical information is limited to the dispensation of prescribed and reimbursed medications that are registered in the insurance records  [1]. Over the counter medications can be easily missed. The French population benefits from universal public healthcare coverage. All information concerning the use of the healthcare system is recorded in the National Health Data System ("Système National des Données de Santé, SNDS")  [4]. Since 2012, the French National Health Insurance has developed a tool based on the SNDS to analyse and monitor the morbidity and economic burden of 58 treated diseases, chronic treatments, and episodes of care through healthcare utilization  [5].
However, such algorithm validity assessments are still scarce. Data from registries offer this opportunity because they provide gold-standard data: they are exhaustive for a given territory, registered manually, and controlled by experienced research assistants. We aimed to assess the performance of five HECM algorithms on patients with ESKD (ESKD, diabetes, HIV infection, cancer, and coronary disease) against information on the French Renal Epidemiology and Information Network (REIN). The REIN database provides national quality-controlled data on patients with ESKD. It relies on a network of nephrologists, epidemiologists, patients, and public health representatives who are coordinated regionally and nationally by the French biomedical agency, collecting exhaustive information on patients with ESKD (treatment and its changes, demographics, comorbid conditions, treatment center location, etc.
The REIN registry The REIN registry was started in 2002 and covered all of France by 2012. It includes all patients receiving renal replacement therapy (RRT) in mainland France and its overseas territories. The REIN database collects information on patient characteristics (body mass index [BMI], age, sex, RRT modality, date of RRT start) and conditions (e. g. , diabetes, coronary artery disease, cancer) based on medical records. Nephrologists, health managers, nurses, medical secretaries, and research assistants collect the data. Continuous controls are ensured during the year (with a strict focus on inclusion criteria, which excludes patients with acute renal disease). Yearly updates are performed to allow the inclusion of new information on patient treatment status, as well as comorbid condition updates.
The SNDS The SNDS (a medico administrative database) collects individual data from various French health insurance schemes. This database contains exhaustive expense and reimbursement information on hospitalizations, ambulatory care, medications, laboratory analyses, and consultations for both public and private healthcare facilities, as well as transportation, compensatory daily allowances, and third-party compensatory indemnity, regardless of the payer of the services (state, complementary insurance, or out of pocket). It does not record primary care consultation diagnoses, or clinical results. For reimbursement, the SNDS includes information on long-term chronic diseases (LTD, a status that guarantees 100% reimbursement for healthcare expenses related to the disease when reported, given the fact that the patient could already been considered for LTD due to another medical condition)  [4].
The HECM applied to the SNDS database uses discharge diagnoses, as well as the chronic diseases registered for healthcare reimbursement and/or specific medical acts/drugs to identify patient conditions (different algorithms for each condition, see details in supplementary Table  1 ). These algorithms are applied to all beneficiaries of the health insurance regimens in France (66. 3 million inhabitants) that have used the healthcare system at least once during the year of interest. The pathologies, chronic treatments, and use of healthcare identified are, for the most part, non-exclusive, as the same person can be affected by several pathologies  [5].
Study population We included patients over 18 years of age that started RRT (either dialysis or renal transplant) in France in 2018 identified through the REIN registry and who could be linked to the SNDS database. Independently of the present study, all REIN patients were matched with SNDS patients over the available extraction period, i. e. 2006-2020 by the national coordination of REIN. with an indirect deterministic linkage that uses a combinations of 6 items: sex, age, location of residence, date and facility of kidney transplant/or start of dialysis treatment, and date of death, if available, with varying granularity (age ± 1, location at municipality or district, date ± 2 months, exact facility or facility in the same district). Further details on the linkage procedure applied yearly can be found in Raffray et al.
Health conditions compared For the purposes of this study, the conditions identified in the REIN registry were considered as the reference. The following conditions identified in both the SNDS and REIN registry were included in this study: ESKD, diabetes, HIV, coronary disease, and cancer (see definition for each in Supplementary Table  1 ). These conditions were selected, as their identification method in both databases were comparable. In addition, the conditions studied presented an opportunity to explore the performance of the algorithms' with different characteristics. Diabetes and HIV are disease specific and likely to be well identified in pharmaceutical records, one being very frequent, whereas the other is less. Coronary disease identification relies on mainly clinical criteria and cancer because it represents a combination of both cases.
Therefore, only true positives and false negatives could be calculated for the item ESKD. To account for the fact that HECM algorithms were designed for medico-economical purpose and individuals may not have been taken into account when they are treated at the beginning or end of the year, a secondary analysis was performed for subjects whose comorbidity data did not match for the year 2018. In these cases (unmatched conditions for 2018), the comorbidity information from the HECM for the year 2017 and 2019 were taken into consideration and new comparisons were performed. As an example, if a patient with a diabetes status did not match for the year 2018, we considered their HECM diabetes status for the year 2017 and repeated the comparison for the whole population. This secondary analysis was carried out for all conditions.
Ethical approval The REIN registry creation was approved by the relevant French committees: the Comité consultatif sur le traitement de l'information en matière de recherché (CCTIRS N°03-149) and the Commission nationale de l'informatique et des libertés (CNIL N° 903,188). The French national health insurance (CNAM) in charge of the SNDS (Système National des Données de Santé) has permanent access to the pseudonymized reimbursement data in application of the provisions of articles R. 1461-12 et seq. of the French Public Health Code, with rules and criteria similar to the Helsinki declaration and permanent full access to the SNDS by decree (Décret n° 2016-1871 du 26 décembre 2016 relatif au traitement de données à caractère personnel dénommé « système national des données de santé»). The CNAM has authorization to perform studies based on SNDS data from the CNIL (National independent Commission for Computing and Freedom, the French data protection agency for sensitive information).
Results In total, 8,309 individuals were identified as incident patients in the REIN registry for the year 2018 (present in both databases). Among them, 5,971 patients were included in our study because of good linkage between the REIN and SNDS databases. The excluded population (those without good linkage) was more likely to include those who died in the year of their diagnosis, started RRT with dialysis, were older, or were a resident of Ile-de-France (Paris region) (Table  1 ).
ESKD status With the HECM 2018 81% of the subjects with ESKD were true positives. In a secondary analysis that included information on the ESKD status from the HECM for 2019, the percentage of patients correctly identified by the SNDS database increased to 93% (Table  2 ). The 1,126 false negative ESKD patients (HECM 2018) were more likely died in the year they started treatment, started treatment with dialysis, among the older population, residents of Ile-de-France, and classified in the SNDS database as having acute renal disease (Table  3 ).
Diabetes Forty-two percent of the population identified in the REIN database were registered as having diabetes. Eight percent of the population's diabetes identification differed between the databases (distributed equally between false positives and false negatives) for their diabetes status between the two databases for HCEM 2018 (Table  2 ). The population of 530 patients with differing diabetes status had a higher proportion of patients who had transplantation as their first RRT, were over 75 years of age, or were residents of Ile-de-France (Table  3 ). The Kappa coefficient of agreement was found to be very good (82%), as were the specificity, sensitivity, NPV, and PPV (over 89%). No great improvement was observed when including the patients' diabetes status in the HECM for 2017 or 2019.
HIV infection Only 1% of patients identified in the REIN database were HIV positive. Approximately 0. 4% of the population differed between the databases based on their HIV/AIDS status (Table  2 ). Among the 21 disparate patients based on HIV status, no transplant patients were misclassified, a higher percentage were aged between 45 and 64, and most were identified as residents of Ile-de-France (Table  3 ). This comparison showed a good Kappa coefficient of agreement. The sensitivity and PPV were the lowest among the other parameters measured, with 83% and 66%, respectively. An improvement to 0. 2% was observed for the false positives when including information from the HECM the year before and after recruitment.
Coronary disease Twenty four percent of the patients identified in the REIN database were recorded as having coronary disease. Fifteen percent of the population differed on coronary disease status, of which two thirds of the disparate patients were false positives (Table  2 ). The 872 unmatched patients based on coronary disease status were more likely to be patients who died early or started treatment with dialysis (Table  3 ). The sensitivity was 79% and specificity 87%. The Kappa coefficient of agreement between the REIN and SNDS databases on coronary disease was 62%. The level of agreement improved to 75% and 69% when considering the information from 2017 and 2019 from the HECM, respectively.
Discussion In this study, we compared the information on patient conditions between the REIN registry collected based on clinical data and the HECM algorithm based on health consumption reimbursement data. The agreement between diagnoses as identified by the REIN and the SNDS varied between conditions, with the highest for diabetes and the lowest for cancer. Specificity was above 85% and the PPV over 95% for all conditions, suggesting overall good performance of the HECM algorithms in identifying the conditions of interest in this study.