dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments The authors like to thank the  Scientific Committee of the Latin America  and the  Caribbean Code Against Cancer  ( Liz Maria   de Almeida (INCA, Brazil) ,  Silvina Arrossi  ( CEDES, Argentina ),  Patricia J Garcia  ( UPCH, Peru ),  Maria Luisa Garmendia  ( University of Chile ),  Alejandro Mohar  ( INSP, Mexico ),  Raul Murillo  ( Centro Javeriano de Oncología, Colombia ),  Julio Santamaria  ( CHOPO, Panama ),  Guillermo Tortolero-Luna  ( Universidad de Puerto Rico )), the Scientific Experts and the Chairs of the Scientific Expert Working Groups ( Juan Rivera Dommarco ,  INSP, Mexico (Lifestyle ),  Catterina Ferreccio ,  ACCDIS, Chile  (Environment and Occupation),  Rolando Herrero ,  ACIB ,  Costa Rica  ( Infections ),  Maribel Almonte ,  WHO, Switzerland  ( Medical Interventions ),  Carolyn Finck  ( Communications )), and the Advocacy Group ( Eduardo Cazap  (Chair) ( SLACOM, Argentina ),  Gabriel O Owen  on behalf of  Maisha Hutton ,  Healthy Caribbean Coalition, Barbados ,  Diego Paonessa  ( ALICC, Argentina ),  J Walter Zoss  ( RINC-SLACOM, Brazil )).
Acknowledgements We thank the following institutions/facilities for their participation in the dissemination of the study : regional health agencies:  Provence Alpes Côte d' Azur, Brittany ,  Ile de France ,  GHT : Alps ,  Dauphiné ,  Rhône center ,  South Drôme ,  Ardèche ,  Western Brittany ,  South Brittany ,  Upper Brittany ,  South.
Study design We conducted an initial audit (A1) on medication prescriptions, preparations, and administrations. Results of A1 were analyzed, and the initial rate of ME was determined. After that, audit feedback was provided to the teams in the form of a slide show. It presented main results of the first audit with positive and negative points observed for the different stages of the drug management (DM) audited. It also outlined ways to improve stages with the least amount of compliance. A satisfaction questionnaire on audit feedback was developed and collected. The following data were reviewed: responder position held, seniority, previous participation in an audit feedback, usefulness of the audit feedback to feel concerned, need for tools, and format of tools needed. The results of this first audit and the satisfaction questionnaire led to develop tools for the proper use of medication (PUM).
These documents were validated by an expert hospital pharmacist experimented in pediatrics, the head nurse, three nurses, and two volunteers who were senior physicians. After tool distribution, a satisfaction questionnaire on the tools was drawn up and sent to the team members. The following data were reviewed: awareness of the audit feedback, frequency of reproduction of this type of experience, experience with this type of training, usefulness to the drug PUM, frequency of the use of the tools, and satisfaction with the tools rated out of 10. A period of tool appropriation (3 weeks) was left before the second audit (A2). A2 was performed to evaluate the impact of the implementation of training sessions and pharmaceutical tools on the occurrence of MEs.
Data collection Audit data were collected using evaluation tables created specifically for this study. An observation corresponds to the audit of a drug prescribed to a given patient and a given route of administration. All the steps of the DM (i. e. , prescription, preparation, and administration) were audited. With this method of collection, the same prescription for the same patient could be audited several times with a different preparation and administration. All observations were included in the analysis. Data collection consisted in the following: • 34 criteria for the parenteral route: 4 for prescription, 15 for preparation, and 15 for administration • 23 criteria for the oral route: 4 for prescription, 7 for preparation, and 12 for administration • 18 criteria for the pulmonary route: 4 for prescription, 3 for preparation, and 11 for administration • 16 criteria for the rectal route: 4 for prescription, 3 for preparation, and 9 for administration.
Statistical analysis and ethical approval Descriptive analyses were conducted in full population and in four subgroups of administration routes (parenteral, oral, pulmonary, and rectal). Data were presented as numbers and percentages or means+/standard deviation (SD). Qualitative variables were compared using the chi-2 test (or Fisher's exact test if theoretical numbers were less than 5). Quantitative variables were compared using the Mann-Whitney test. For all the tests performed, the risk of consented error was set at 5%. Statistical analyses were performed with SAS v9. 4 software ®. This study was approved by the Institutional Review Board of Montpellier University Hospital (IRB number: 202000620).
and over residing within the walls. Names and addresses were provided by the municipal authority through the census lists. Following an invitation letter signed by the town's mayor, explaining the nature of the study, an employee visited each family to illustrate the study's aims and procedures and to make an appointment for all family members willing to participate. In order to gather data into families, the invitation was extended to firstdegree relatives (parents, siblings and offspring) residing outside the medieval city walls. A baseline examination (Exam 1) was performed between 1983 and 1986, with the participation of a total of 5376 individuals with a response rate exceeding 93%. The first follow-up examination (Exam 2) was performed between 1989 and 1992. The second follow-up examination (Exam 3) was performed between 2001 and 2007.
Biochemical measurements were assayed by enzymatic methods that was measured by kinetic alkaline picrate assay: they were partly under external control of the WHO Lipid Reference Center of Prague. Marital status. At inclusion, marital status was recorded by questionnaire, consisting of 7 categories: married, conjugal living, single, widowed, separated, divorced or unknown. In the present analysis, we classified the population at entry into two categories: married subjects -which included married and living in conjugal status and unmarried -i. e. subjects who were single, widowed, separated or divorced. Subject with unknown status were excluded. Follow-up and outcome definition. The follow-up was conducted up to 20 years after the entry exam. CVD was identified using data collected in subsequent examinations and -for those who did not attend -it was identified with home visits, review of hospital records, interview with subjects, family physicians and/or telephone interview.
Some studies are restricted to older patients while other are oriented to younger and healthier subjects. Some are designed as retrospective studies in which all data regarding medical conditions as well as psychosocial and other risk factors are extracted from medical records. We considered here the marital status only at inclusion and not during the follow-up, combining married and conjugal couples whereas the counterpart was formed by single, widowed, separated and divorced. A single assessment of marital status at baseline, instead of repeated ones during follow-up, however, is probably sufficient in our analysis of   2  and 3. this long-term follow-up study because remarriage is a relatively rare event in the Mediterranean population especially during the 90's (i. e. < 5%)  5. It is known that among catholic and orthodox people marriage is strongly related to religion.
Impedance Measurement Body composition was measured with the multifrequency bioelectric impedance (Biody XpertZM II, AMINOGRAM SAS, la Ciotat, France). All measurements were recorded during the same morning. No vigorous to intense PA was performed 12 h before the test. Adolescents had an empty bladder and did not consume alcohol or caffeine-based beverages at least 24 h before the test. The measurement protocol was standardized and explained to everyone. The adolescent, in a seated position, placed his right hand on the rear sensors of the bioelectrical impedance and placed the front sensors at the level of the rear foot under the ankle. The raw results (rate (kg), percentage of FM (%FM), and percentage SMM (%SMM were then provided by the "BiodyManager" (version 1) software. The CRF was assessed by the CRF on the adapted 20 m shuttle run/walk test (TMNA-20)  [33].
Statistical Analysis All statistical analyses are carried out by Statistica software (version 7. 1). The mean, standard deviation, median, and distribution of the data are calculated for each variable: age, BMI, %FM, %SMM, maximum aerobic speed (Vmax), aerobic capacity, PPL score (points), and PA levels (MVPA per week)). The normality of each variable was tested using the Shapiro-Wilk test. Due to the non-normality distribution, the non-parametric Mann-Whitney U test is used to test the gender-dependent variations in each variable. A p-value ≤ 0. 05 was chosen for statistical significance. Simple linear regression models are used to determine the relationships between PPL (independent variable) and the different dependent variables: BMI, %FM, %SMM, aerobic capacity (mL•kg•min -1 ), and MVPA (min/week).
Acknowledgments: The  CAPACITES 64  project is funded by the  Conseil Départemental des Pyrénées Atlantiques  ( 64 ) in association with the  Centre Hospitalier de la Côte Basque (CHCB) ;  Unité Transversale des Activités Physiques pour la Santé (UTAPS)  service and the  Université de Pau et des Pays de l'Adour (UPPA). The CAPACITES 64 project started in 2019 and will continue until 2024. It is in France on the whole department of  Pyrénées Atlantiques  ( 64 ). This project is supported by the  Conseil Départemental des Pyrénnées Atlantiques  ( 64 ). Its objective is to develop and transfer a program of accompaniment for schoolchildren in difficulty about physical activity.
Participants and procedures For this validation study, routinely collected data were available from two sample sites (Hangzhou and Ningbo, China) and contained an assessment of sleep using the below three measures from December 2020 until January 2021. The trained investigators were responsible for the conduct of the survey and its onsite quality control. Self-administered paper-and-pencil survey was centralized at recess or evening self-study. Healthcare students were recruited by applying a stratified random sampling approach based on their academic years and majors  [21]. Inclusion criteria: individuals who were able to read simplified Chinese and communicate in Mandarin. Exclusion criteria: 1) people who were reluctant to participate; 2) those who had difficulty understanding the study procedures.
Data preparation Data were checked for data entry errors, missing data, or the presence of extreme outliers. Frequencies (%) were calculated for categorical variables, whereas means and standard deviations were computed for continuous variables. Multivariate normality was assessed via skewness and kurtosis. Data analyses were performed with JASP (v. 0. 16. 1) and R (v. 4. 1. 2). The packages "naniar v 1. 0. 0"  [32] , "MVN v. 5. 9"  [33] , "lavaan v. 0. 6-9"  [34] , "semTools v. 0. 5-5"  [35] , "irr v. 0. 84. 1"  [36] , and "ufs v. 0. 5. 2"  [37]  under RStudio were utilized to conduct the missing value analysis, multivariate normality tests, confirmatory factor analysis (CFA), longitudinal CFA (LCFA), intraclass correlation coefficient (ICC), and Cronbach's alpha as well as McDonald's omega.
Descriptive We analyzed final data from the matched sample of 911 healthcare students (complete data response rate 93. 34% and 95. 79% for T1 and T2, respectively). The average time interval between T1 and T2 was 7 days + 5. 37 h. The mean age of participants was 19. 66 ± 1. 45 years, ranging from 17 to 31 years. Additional descriptive information is presented in Table  1. Means, standard deviations, skewness, kurtosis, and amount of missing data at T1 and T2 are presented in Additional file 1: Table  S1.
Acknowledgements The authors appreciate  Daniel J. Buysse (MD, Professor ,  Department of Psychiatry, University of Pittsburgh, USA ) and  Carolyn Weber  ( MBA, Licensing Associate, Innovation Institute, University of Pittsburgh, USA ) for use permission of the RU_SATED scale. The authors appreciate three reviewers and Editorial Board Members for their helpful suggestions. The authors thank  Yi Luo  ( MSN , RN , Associate Professor,  School of Nursing, Ningbo College of Health Sciences, China ) for data collection,  Tuo Liu  ( PhD Candidate ,  Institute of Psychology, Goethe University Frankfurt, Frankfurt am Main, Germany ) for discussion on statistical analysis, and  Chen Jiang  ( Master   Candidate ,  School of Public Health, Hangzhou Normal University, China ) for assistance in data visualization.
For example, in France, a national perinatal survey on population samples is carried out at regular intervals  [15]. A series of periodic cross-sectional national surveys on child health has also been carried out for various samples of school years and periods  [16]. Finally, a national cohort (Elfe) follows 18,000 children selected at birth in 2011 to study determinants of the development, health, and socialization of children from birth to adulthood through a multidisciplinary approach  [17]. Thus, faced with a lack of such information, the French Court of Auditors noted that the analysis of a nationwide health and healthcare dataset of children that includes, among others, chronic disease diagnoses and related care, and details the use of healthcare services and the mortality of live-born children according to socioeconomic status is still needed to better understand inequalities in the access of children to healthcare  [18].
Design and study population This retrospective observational study included children < 18 years of age in 2018 and followed up for one year between 2018 and 2019 according to their births or birthdays. In mainland France (population of 64. 9 million), there were 13. 99 million children < 18 years of age on the first of January 2019, according to the INSEE [Institut national de la statistique et des etudes economiques, National Institute for Statistics and Economic Studies]  [26]. Those excluded from the analysis included children who received no refund during the one year follow-up period and those of the same sex resulting from a multiple birth (twins, triplets, etc. ) who could not be unambiguously identified in the database, as those with a false number of linkages or those lacking critical information, such as the month of birth (N = 13.
Data source The SNDS collects individual information from the various French health insurance schemes  [19]. It does not record office consultation diagnoses or the results of clinical examinations and investigations. Nevertheless, it includes information on the presence of LTDs. Using a pseudonymised identification number, all this information is linked, via the national hospital discharge database, to data concerning public and private hospital stays: short-stay hospitalisations (SSHs), stays in psychiatric hospitals or rehabilitation facilities, and home hospitalisation. Normal stays in hospitals/maternity units for childbirth without the need for neonatal care were excluded. Information is also collected for various SSH units, such as the intensive care unit (ICU, paediatric or adult), neonatology, and emergency department (ED) visits followed or not by admission for SSHs.
Social deprivation The FDep is divided into quintiles computed in 2015 (Q1: least deprived, Q5: most deprived)  [20]. It was constructed at the "commune" scale (smallest administrative unit, 36,000 units in mainland France and similar to a municipality) according to four factors resulting from data published by the INSEE: average household income, the percentage of high school graduates (graduates in France who passed the Baccalaureate exam [national exam required to obtain a high school diploma]) among inhabitants aged 15 and over, the percentage of blue-collar workers in the active population, and unemployment levels. In addition, we calculated the primary healthcare practitioner density between quintiles (per 100,000 inhabitants). Among the study population, the FDep was missing for 1. 1% of the children, mainly due to the absence of information on the commune of residence.
The global one-year live birth mortality rate (0-1 year) and the population were also reported for the deprivation levels. Data on the SSH primary diagnoses are expressed as the rate among the population that had at least one SSH stay after gender and age-standardization using the same population. To assess the intensity of healthcare use, the median and interquartile range (IQR) of the number of utilizations are reported for children who used healthcare services at least once during the year. SAS software (version 7. 13, SAS Institute Inc, Cary, NC, USA) and R software (3. 4. 3) were used for statistical analysis.
Variables collected Data collection through chart review started in August 2022 and ended in February 2023. The variables were collected from medical files of patients and included age, sex, smoking status, number of visits, duration of follow-up, BMI, diabetes, coronary artery disease (CAD), systolic heart failure, SBP and DBP levels of all visits (if there was a modification of treatment, SBP and DBP levels before any modification were collected), laboratory measurements of serum creatinine and albumin over creatinine ratio (ACR) at first visit T1 and last visit T2, mean monthly outdoor temperature during each visit based on S1 Table. Antihypertensive treatment type was recorded: CCB, ARB, ACE inhibitor, beta-blocker, thiazide, furosemide, spironolactone, moxonidine, alpha-blocker. All changes in antihypertensive medications were collected: dose reduction or increase, removing or adding a medication, change of medication.
Statistical analysis The Statistical Package for the Social Sciences (SPSS) version 25 was used for data analysis. Categorical variables are presented as numbers and percentages. Continuous data is reported as mean and standard deviation (SD) if it has a normal distribution, and as median and inter quartile range (IQR) if the data is not normally distributed. A paired-T test analysis was used to assess the variation between summer and winter blood pressure values. McNemar's test was used to compare paired categorical variables. A linear regression analysis was used to assess the association between seasonal variability and the change in eGFR. A logistic regression analysis was used to assess the relationship between seasonal variability and dialysis or death. Variables that had a significant p-value in univariate analysis were adjusted to demographics, eGFR, SBP, DBP, and number of antihypertensive treatments when included in the multivariable analysis.
Strengths and limitations This study is the first to assess blood pressure variability in a Mediterranean climate. It is also one of the few assessing a difference between CKD and non-CKD patients among patients diagnosed with hypertension. Furthermore, it includes a representative sample size of the Lebanese hypertensive population. The limitations of this study are those related to its retrospective design and the lack of data on ambulatory blood pressure levels. In addition, the room temperature where the blood pressure was measured was not documented but we believe that it correlated with the weather temperature across seasons.
+89 attributable to heat in the modeling. Between 2014 and 2020, 23,762 deaths were attributable to heat in France using our models, vs 21,444 deaths based on the European climate change and health observatory indicator (Table  2 ). Estimates were largely consistent between the two sources after 2017. intervall of the MMT (data not showned). The European Observatory on Climate Change and Health observatory used the MMT, and nd estimates consistent with ours. In this study, we focus on the departmental level, which can mask sub-departmental contrasts in exposure and potentially in vulnerability and impacts. Although the departmental reference station of Météo-France is supposed to be representative of the exposure of the majority of the department's population, sub-departmental temperature variations can be signi cant, especially for coastal or mountainous departments or in areas experiencing a large urban heat island.
Excess mortality during heat waves is estimated annually by Sante publique France (Wagner et al. 2018), based on the comparison between observed and expected mortality. Those data were extracted for each department from the public database geodes. santepubliquefrance. fr for 2014-2022. The European climate change and health observatory proposes an estimation of the annual number of heat-related deaths per millions inhabitants per country, based on an approach develop for the Lancet climate countdown (van Daalen et al. 2022). Annual estimates for France were extracted for 2014-2022, and transformed into annual number of heat-related deaths based on the annual French population provided by INSEE.
e. , changes in job tasks within an occupation), this particular dimension has remained relatively under-explored due to data limitations. The present study fills this gap by creating a measure of routine-task orientation for 322 occupations based on two main data sources for the United States (US), namely, the Dictionary of Occupational Titles (DOT) and its successor, the Occupational Information Network (O*NET). Such a measure allows us to build a consistent time series of within-occupation task changes over a thirty-year period, from 1980 to 2010. Using this measure, we shed light on unexplored aspects of the long-term structural changes in the US labour market spanning various phases of technology diffusion. Within-occupation task change is by definition a long-term phenomenon that requires the mutual adaptation of demand and supply of skills.
The paucity of suitable data sources offers a cue to the first contribution of the present paper. A thorough analysis of how job content evolves requires a data series that covers a long timespan. The two most frequently used resources are the DOT, which was updated until 1991 when O*NET, the second source, was officially released. Despite being designed for a similar purpose, however, the match between these two resources for the purpose of generating a long time series presents some challenges. A key issue is that the complexity of the data has increased significantly with the inception of O*NET, so that using task items from these two sources, while maintaining consistency, requires a high degree of discretion on the part of researchers  (Autor, 2013). Section 2 details the procedure for the identification of matching items in DOT and O*NET that allows us to construct a time-varying measure of job tasks for 322 occupations.
The guiding criterion to operationalise the matching between the two sources is the similarity between the task title and the task description. Based on this matching, we build an index of routine task intensity (RTI 1 ) that also accounts for within occupation task changes. We show that the proposed procedure is successful in matching the moments of the distributions of the underlying task measures for the two data sources and the different decades. Section 3 presents descriptive evidence of changes in the task content of occupations, using standard data sources, i. e. , the decennial Census and American Community Survey (ACS), to construct weighted averages of occupational task measures. First, we find that within-occupation task change accounts for 37% of the overall decline in routine task use between 1980-2010.