dataesr/scientific-paragraphs-categorization huggingface dataset:
A total of 25 AUCs were thus computed for each network (4 test datasets × 7 classes -3 exclusions). To assess the generality of the MD network, a leave-one-dataset-out experiment was conducted: each of the four datasets, in turn, was set fully aside for testing, i. e. its training and validation subsets were ignored. Next, a statistical evaluation was performed to compare the 25 test AUCs obtained when the corresponding training/ validation datasets are ignored to those obtained when they are used. Since the AUC differences were not normally distributed and the number of paired samples (25) is less than 30, the paired samples Wilcoxon test  35  was applied, instead of the usual paired samples t-test. Note that, in the leave-one-dataset-out experiment, each dataset is excluded from a single 3-tuple of datasets, but it is included in three: the one leading to the lowest mAUC was used in the comparison.
Data availability The data that support the findings of this study may be available from DR screening program of OPHDIAT © and private screening program of OphtaMaine © , but restrictions apply to the availability of these data. These data, or a test subset of them, may be available subject to ethical approvals. The ODIR and the RIADD datasets are publicly available at the following URLs: RIADD dataset: https:// riadd. grand-chall enge. org/. ODIR dataset: https:// github. com/ nkicsl/ OIA-ODIR.
Data collection PROMISE-EPI staff members, after having received training in qualitative data collection, contacted the PROMISE-EPI participants and HCP. They conducted face to face individual interviews and focus group discussions based on a semi-structured guide, lasting about 1 h each, between December 2021 and September 2022 in the PROMISE-EPI study rooms. PROMISE-EPI participants received compensation for travel to the study site and for their time (100Kwatcha = 5 USD). Interviews were conducted in one of the 3 main languages in Lusaka, Bemba, Nyanja or English and were audio recorded. Simultaneously translation in English was done when necessary and transcribed by the interviewer, keeping the anonymity of the interviewee by using PROMISE-EPI ID numbers (or a dedicated number for PROMISE-EPI staff and HCP).
Data analysis An initial code-book was designed using a priori codes from the interview and FGD guide and then adapted on the basis of the emerging themes found in five initially double-coded transcripts. These first transcripts were coded by two researchers (RK and AM) and compared to ensure reliability. Any discrepancies were discussed and resolved. The following transcripts were coded by one researcher (AM) using Excel software and reviewed by the second (RK). A descriptive thematic analysis was chosen to explore and describe the factors leading to PNP barriers and facilitators  (12). Three main themes and seven sub-themes emerged from the data and were related to the study objectives (Table  1 ). Trustworthiness was enhanced through member checks and feedback was also obtained from dissemination with national stakeholders, the study team and PROMISE-EPI staff.
Source Our primary source of data is the website Animap. fr. We used web scrapping techniques in order to recover the pages created by firms between the 16th of April and the 8th of September 2021. 24  did not collect personal information from registrants. We focus and recover the following elements: (a) the name of the firms, (b) the cities where they are active, (c) the category of firms, (d) the description of the firms written by the professionals (if any) and finally (e) the date when the pages have been created. Regarding the categories of professionals, Animap. fr created its own classification that contains 19 possibilities. We primarily use this categorization as often, we were not able to mach firms to categories provided by other taxonomy.
Registrations on Animap. fr therefore seems to corroborate the observation by Kowalewski (2021): protesters had multiple motives to oppose the policy (economical, ideological, religious, etc. ). The map shows the number of registrations on Animap. fr per département. The data were obtained using web scrapping techniques. Our treatment. Remark: we only had data for the entire Corse (and not for the two départements). On this map, we considered that each départements in Corse had half of the registrations.
Results The result of the analysis may be observed in table  4 , where once more we introduce the variables one at the time. In column (1), we consider the four variables that capture the effect of the announcement, in column (2) we add the fifth order polynomial. Then we add the day of week dummies in column (3) and the month fixed effects in column (4). Sources: This table uses the number of registrations per day and per categories on the website Animap. fr. See the data section for more details on the data. Notes: The dependent variable is the cosine similarity between the professional that registered at date d and those that registered before June. All columns provide the results of OLS regressions where we gradually add controls for the dynamics of the registrations on the website. Robust standard errors are provided in parenthesis.
Study design and population The present study relied on the existing data of the COSALI prospective cohort, which was conducted from 2002 to 2010 in the general working population of the Pays de la Loire region in western France, as part of the National Musculoskeletal Disorders Surveillance Program among the working population  (9, 10). Details of this study have been reported elsewhere  (9) (10) (11) (12). Briefly, 83 occupational physicians (OP) volunteered to randomly enroll workers when they came for a medical check-up, which was mandatory at least every two years for all workers according to the French Labor Code during the study period. Subjects were randomly selected using a two-stage sampling procedure. First, the investigators chose 15-30 half-days for each OP. Then, each OP was asked to randomly select 1 out of 10 workers from the scheduled visits on the selected half-days  (10).
Candidate predictors In addition to age and gender, a set of 47 occupational risk factors were selected as potential predictors of UEMSD (supplementary material, www. sjweh. fi/ article/4119, table  S1 ) using background knowledge, literature reviews (3) and previous results on the Cosali Cohort  (5, 11, 12, 14, 15). Individual data on these occupational factors were collected through a selfadministered questionnaire at baseline, completed by workers before their clinical OP examination. Exposures to physical factors were defined using the SALTSA European consensus criteria (13): repetitiveness of tasks (≥4h/day) with/without break, arms above shoulder level (≥2h/day), wrist twisting movements (≥2h/day), arms abduction (60-90°) (≥2h/day), holding hand behind the trunk (≥2h/day), elbow flexion/extension movements (≥2h/day), pronation and supination movements (≥2h/day), use of the pinch grip (≥4h/day), use of vibrating hand tools (≥2h/day), use of computer keyboard or mouse (≥4h/day), exposure to cold temperature i.
Work organizational factors included five binary variables describing potential determinants of the participant's work pace (dependent on automatic rate, dependent on production standards or deadlines, imposed by permanent monitoring, dependent on colleague's work, dependent on external demand). Questions from national surveys on working conditions were used to collect these data  (19). Two factors characterizing work schedule (shift work, irregular working hours), and two factors related to employment status (temporary employment, work with temporary workers) were also retained as candidates. Missing data. Missing values for candidate variables were handled with multiple imputation by chained equations using the R package mice, since preliminary analyses suggested that a missing at random (MAR) hypothesis could be assumed.
In each data set, the optimal value of lambda was automatically determined through cross-validation. Group Lasso approach was used to deal with variables with more than two categories, ie, to include all dummy variables in the model when at least one was selected. Age and gender were forced into the models as personal characteristics, thus the 47 occupational factors were tested as predictors of UEMSD taking into account potential differences in disease prevalence explained by age and gender. The final set of selected predictors was chosen based on the frequency of inclusion of the variables in the models obtained in the 20 sets of imputed data  (22). Retained variables were those included in ≥15 of the 20 models (75%). Then a Lasso logistic regression including only the selected predictor variables was applied to each imputed data set for estimating shrunk coefficients.
Sensitivity analyses Similar predictive performances were obtained from complete case analysis in the validation sample (supplementary table  S2 ). The performance in terms of classification in complete cases were also very close to those found on the imputed data (supplementary figure  S2 ). The prognostic ability of the developed score was further assessed separately for the three main locations of UEMSD. The discriminant performance of the score varied according to the location of MSD: for shoulder disorders (AUC 0. 57, 95% CI 0. 51-0. 63), for elbow disorders (AUC 0. 62, 95% CI 0. 53-0. 70) and for hand/ wrist disorders (AUC 0. 67, 95% CI 0. 59-0. 75).
13 1 Social support: superior not No - 0 concerned Yes 0. 31 3 Social support: unhelpful No - 0 co-workers Yes 0. 38 4 Organizational factors Irregular working hours No - 0 Yes 0. 13 1 Work with temporary workers No - 0 Yes 0. 20 2 Work pace dependent on No - 0 colleague's work Yes 0. 18 2 Work pace dependent on pro- No - 0 duction standards or deadlines Yes 0. 08 1 Maximum score 35 a Adjusted for age and gender. Rubin's rules were used to combine the shrunk coefficient estimates of each predictor obtained in the 20 imputed datasets. b c Reference category. d Items from the Karasek Job Content Questionnaire (18).
g. atrial fibrillation or arrhythmias), or neuromuscular or orthopaedic diseases affecting gait. Cardiac integrity was checked by an electrocardiogram at rest and during a symptom-limited exercise tolerance test on a cycle ergometer. During the entire PR program, the patients were asked to keep taking their usual medication. All included patients signed a written consent form prior to joining the study. The procedures were approved by an independent French ethics committee (CPP IDF7 Ile-de-France, reference number: 2019-A00582-55) and complied with the principles of the Declaration of Helsinki for human experimentation. The protocol was registered on clinicaltrials. gov (Clinical Trial registration number: NCT04279730).
Heart rate variability (HRV) The HRV (i. e. R-R intervals) was recorded in a quiet room at rest and in supine position by a portable heart rate monitor, Polar V800, and with a Polar H10 chest strap (Kempele, OY, Finland). The polar V800 system consists of a watch worn on the left wrist that is connected by Bluetooth to an electrode belt fitted on the chest that records R-R intervals at a sampling frequency of 1000 Hz. Before the start of R-R interval recording, the patient's skin was cleaned and prepared to be fitted with the polar V800 electrode belt. The belt was then moistened and tightly but comfortably adjusted just below the chest muscles in accordance with the instructions for use of the device. The HRV recording lasted 10 min. Only the last five minutes were considered for analysis to ensure that only data from a stable state were taken into account.
The raw R-R intervals were exported from the Polar FlowSync (Polar Electro™, OY, Kempele, Finland) as a space-delimited. txt file. Processing and artefact corrections in the recorded R-R intervals were carried out using the RHRV package of R software  [25]. First, R-R interval series were visually inspected to detect a stable window for analysis of at least 400 R-R intervals, with few artefacts. Putative artefacts were identified from the selected windows and removed if they were not 95% similar to the 10 previous ones and the 10 following ones. In cases where several artefacts were grouped together and not automatically detected, but visually identified, they were removed manually via the RHRV package. After artefact correction, a series of 300 R-R intervals was retained for each participant  [26].
Visuomotor reaction time (RT) The RT was assessed according to the process previously described by Volker et al. [21]  using the free software PsyToolKit (Glasgow University, Glasgow, United Kingdom)  [30, 31]. The participants were seated comfortably and faced a computer screen and a keyboard in a quiet room. The task consisted of pressing the space bar on the keyboard as quickly as possible whenever a black cross appeared within a white square on the computer screen. Each evaluation session started with a short training session comprising five stimuli. The main task was then started, with a total of 15 stimuli that were presented at random time intervals varying from 2500 to 3500 ms. The average RT was calculated after removal of any aberrant responses (RT < 150 ms or > 1000 ms). The RT is known to increase with the occurrence of fatigue  [32].
Statistical analyses All statistical analyses were performed using Statistica software (StatSoft, Inc. , version 6. 0, Tulsa, OK, USA), unless specified otherwise. The changes in functional exercise capacity, multidimensional fatigue, HRV, and RT during the PR program were assessed using one-way analyses of variance (ANOVA) for repeated measures. The underlying assumptions of ANOVA were checked using Skewness-Kurtosis coefficients (normality of distribution) and the Mauchly test (sphericity of variance). In case of violation of the assumptions, non-parametric Friedman tests were used. When the analyses revealed a significant difference between the five times, Tukey post hoc analyses were conducted (or the Wilcoxon test with Bonferroni corrections in case of non-parametric data). The associations between the 6MWD and the different fatigue indicators (MFI-20 total score, mean RT, and HRV variables) at each time point were assessed with Pearson's test.
Study participants and recruitment Data were collected from adolescents and youth aged 15 to 24 between June and October 2019. Both gender were included as: women bear the greatest burden of pregnancy-related health problems and are the primary users of contraceptive methods; and men have an in uence on women's decision making, plus they can make a decision about male condom use. That is, men also have a responsibility for contraception. Moreover, as Couple involvement in the sexual and reproductive health program can promote the adoption and use of contraceptive methods in patriarchal societies (18), the inclusion of men in our explorative study is even more justi ed. To be included, they needed to live in the city of Conakry. No exclusion criteria was applied : they could either be users or non-users of contraception methods.
Participants were purposively selected to diversify their socio-demographic pro le (i. e. , gender, level of education, and marital status), in order to capture a variety of practices, experiences and perspectives. They were contacted through the administrative hierarchy of each commune, i. e. the mayor, the district and sector chiefs. In each commune, three neighborhoods were selected based on the availability of the neighborhood heads and the perceived ease of access to adolescents and youth. Within each neighborhood, three sectors were randomly selected by the data collection agents. Adolescents and youth in each sector were put in contact with the collection agents by the sector heads. Following this contact, interviews were scheduled at the convenience of the adolescents and youth.
Data collection Data were collected through two modes: individual in-depth interviews (IDIs) and focus group discussions (FGDs). Individual interviews allowed for thorough exploration of participant's personal story. This mode is also more appropriate for discussing personal or taboo topics such as sexuality. Focus group discussions allowed to stimulate discussion among participants, as well as to enrich the data collected individually. The individual and group interview guides incorporated the following domains: previous and current FP use and experience; general knowledge about the FP method participants use (e. g. , advantages and disadvantages); attitudes of relatives about contraception methods; experience with FP services (including with FP service providers); and participants' demographic and socio-economic characteristics (age, marital status, education, occupation and commune of residence).
Acknowledgements The authors would like to thank the  International Union for the Scienti c Study of Population  for nancially supporting research design and implementation, and the the Belgian Cooperation and  Development Directorate  for nacing data analysis workshop. We also thank colleagues of  the Centre National de Formation et de Recherche en Santé Rurale de Maferinyah (CNFRSR)  for their technical support and guidance during this study conduction.
Discussion We analyzed questionnaire data on 4500 newly performed or more frequently performed occupations submitted by 2534 individuals (YAs: 47%, MAs: 33%, and OAs: 20%) across Europe (mainly France: 78%). Our results showed that YAs were more likely to engage in new occupations than OAs, who were more likely to engage more in previously practiced occupations. Overall, the occupations that were more frequently engaged in during lockdown than during the prepandemic period were leisure occupations (67%), followed by productivity occupations (31%) and then selfcare occupations (2%).
We also adjusted for the country in our multivariate model; there were no significant differences other than for paid/unpaid work. We therefore decided to pool the data from all countries. Thirdly, the sample was predominantly composed of women (77%). Occupations can differ between genders  [9, 11, 32]. In particular, the literature data show that women's engagement in leisure activities is less affected by age and worsening health than men's engagement is  [33]. However, all our results were adjusted for gender, which enabled us to interpret our results with regard to the generation. Fourthly, we followed the COPM process as closely as possible, even though the tool's psychometric proprieties were suited to an interview rather than a survey. For the same reason, we collected less information than is usually gathered in a COPM interview and so had to decide on the allocation of an occupation (rather than discussing it with the respondent).
Data and methods This study uses a case study approach, focusing on two mining sites in La Guajira and Cesar to address the multiple complexities of COVID-19 in Colombia. The case study approach was selected to capture and examine the micro-dynamics of the phenomena at the national and subnational levels  (Yin, 2003). The proposed analysis adopts a transformational approach to resilience, focusing on the ability to 'bounce-back' to normal after a crisis and 'bounce forward' by responding and readjusting to shocks by elaborating on new capabilities  (Trump and Linkov, 2020). The information used for this study was collected in two phases. In the first phase, we systematically reviewed official reports from national and international agencies, national and regional newspapers, and other databases and media sources between March 2020 and January 2021.
We performed document and archive research searching for the combinations of the following keywords: 'COVID + Coal + Ethnic Minorities + Indigenous' (COVID + Carbón + Indígena + Minorías Étnicas) in multiple databases focused on Indigenous issues provided by international, national, and regional agencies (see Table  1 ). The search also included the most important organizations such as ONIC (Organización Nacional Indígena de Colombia) and CNOA (Conferencia Nacional de Organizaciones Afrocolombianas). This phase led to collecting and reviewing 40 reports, official papers, and 474 news articles in Spanish and English. All reviewed sources and documents were coded through deductive content analysis, a systematic tool describing phenomena and a widely used method of analysing documents  (Myers, 2009).
For these countries, the data included detailed comprehensive utilization and cost data across five care components -inpatient hospital care, facility-based post-acute rehabilitation, primary care MD visits, outpatient specialty MD visits, drugs -for two specific high-need high-cost patient cohorts. As further detailed in work by Figueroa et al. ,  [8]  these patient cohorts consisted of a frail older adult (65+) with a hip fracture and an older person (65-90) with complex multimorbidity, including heart failure and diabetes. Using the ICCONIC data, we were able to make comparisons of yearlong health system expenditures for these clinically similar patient cohorts. The granularity of information in data allowed us to further explore the contribution of care volume, care type, and price levels to overall expenditure.
Data Individual patient level data from 2016 to 2017 were obtained from six countries participating in the ICCONIC collaborative: Canada (Ontario), France, Germany, Spain (Aragon), Sweden, and the US (Medicare fee-for-service) (Appendix 1& 2). Across countries, the data spanned five care components, inpatient hospital care, facility-based post-acute rehabilitation, primary care MD visits, outpatient specialty MD visits, and drugs, and included information on patient characteristics, comorbidities, utilization, spending, and outcomes  [8]. Data were obtained for two patient cohorts selected to represent individuals with high-needs and high-costs, such that they will use care across health services, and that their costs represent a significant proportion of overall health spending, see for example Tanke et al.
In some countries, samples payment rates set by specific payers were used to calculate prices, such as Medicare fee-forservice data which were used to impute prices for hospitalization and a specialist visit in the US. Similarly, France used administrative claims data from the national sickness fund to impute prices for hospitalisations and specialist visits. This may lead to bias in results for some countries where these rates significantly differ from other providers, such as the US where federal payment rates have been shown to differ from negotiated prices in commercial insurance  [12]. Detailed information on the data sources and the representativeness of the datasets are outlined in Appendices 1 and 2. Routinely collected data linked across care settings were used to examine the entire care pathway for the two cohorts for 2017, the reference year.
The Fisher-type indexes between each pair of countries were then converted into transitive, invariant multilateral indexes using the Elteko-Koves-Szulc (EKS) method  [14, 15]. Those indexes are the treatment episode parities or PPPs. To examine how much health system expenditure for the patient cohorts is attributed to price and volume variations, we applied different PPP indices to the expenditure data obtained for each cohort. Taking the overall expenditure across all five care settings over the course of 365 days following their index admission, we first standardised the data using AIC PPPs using the US as the base country. We then compare these estimates to hospital expenditures standardized using Health PPPs and the specific-treatment episode PPPs. Each of these indices will convert expenditures to the uniform price levels as indicated by the respective baskets of goods and services and allowing the relative comparison of expenditure to reflect only the difference in the volume of goods and services consumed.