dataesr/scientific-paragraphs-categorization huggingface dataset:
Procedure The study was registered with the Commission Nationale de l'Informatique et des Libertés (CNIL). Ethical standards were maintained throughout the study, written consent was secured from sports club directors and key informants. All participants were identified with a code number to protect their identity. No individual data on intervention managers was collected, except their position within the club and employment. Different steps were undertaken: (1) an initial interview to describe the intervention, (2) document analysis, (3) intervention mechanisms and stakes analysis. Step 1: After an initial contact email to the sports club's intervention manager, a short phone interview (conducted by AVH, FL or FR and LC) helped to (1) document the intervention and ensure fit with inclusion criteria, (2) collect documents and the club's website address for intervention comprehension and (3) transmit all ethical documents, including informed consent and an intervention information letter.
Data Analysis Qualitative data from all interviews were digitally recorded, fully transcribed and analyzed using a paper and pencil approach, to accomplish the organization and coding process. Each sports club's transcription and collected documents, including information gathered from their website, were read twice to underline key ideas. Then, the Health Promoting Sports Club (HPSC) intervention planning framework  [27]  was used to analyze interviews and accompanying documents (sports clubs' regulations, educative project, submitted funding application). The deductive coding approach  [34, 35]  was applied to identify which of the 14 strategies and underlying 55 intervention components from the HPSC intervention planning framework  [27]  were cited and how they were implemented, by categorizing the quotes into a strategy and linking it with an intervention component.
Qualitative study 1: preliminary study with experts Methods The first study is based on four semi-structured interviews with subject experts (a scientist and a journalists) and two knowledgeable laypeople (two patient organization managers) (Table  3 ). Individual semi-structured interviews lasting 60-80 min were carried out from February to May 2015, using an interview guide including several themes (Additional file 1). Each expert provided a complementary vision of the issues involved and the means of empowering patients, thus ensuring a diversity in the points of view expressed. The qualitative data analysis method is based on a manual thematic content analysis (intra-and interinterview)  [67]  and derived from the Spiggle framework  [68] , which involves several distinct operations to organize data, extract meaning, and arrive at conclusions.
It also made it possible to link semantic markers to the evolution of the messages and each participant's journey. We carried out two complementary content analyses (thematic and lexical). The first was vertical, one forum participant at a time. After a scan of the 21 participants' discussions, and based on the results of the first study (distinguishing individual, identity, collaborative, and productive empowerment), we divided the content of each participant's messages into four categories. A chronological analysis showed similar progressions for all the participants studied. In the second step, a horizontal content analysis was carried out, grouping posts according to the stages of empowerment identified in the first study. To complement the manual thematic content analysis, lexical analysis software (Alceste) was used to identify the key words or textual units used that were representative for each category.
| DISCUSSION The present scoping review identified studies that investigated communication tools for patients and their impact on patient satisfaction in the field of esthetics in dentistry. During the review process, a considerable amount of case reports presenting different available tools were found. Even though case reports and studies with less than 10 participants were excluded to elevate the evidence level, 10 clinical observational studies met the inclusion criteria. [32] [33]  Today, approximately 15 smile design software are available for dental professionals. 30 In particular, AR has gained access to healthcare and gradually plays a role in dentistry, too. Most studies included in this review were published by Scandinavian research groups, such as Sweden and Finland. One potential explanation for the leading role of these countries in respect to patient involvement in healthcare may be the Nordic leadership style, which comes along with a flat hierarchical structure in Scandinavian countries, and might promote a less paternalistic patient-dentist relation.
graphic databases were searched: Medline, Embase, Cochrane, and World of Science from January 1, 2000 to March 3, 2020. The search strategies were drafted by an experienced librarian (MVB) and further F I G U R E 1 Derivative sequence of key words refined through team discussions (Figure 2A-D). The final search results were exported into EndNote (Clarivate Analytics, London, UK), and duplicates were removed using Covidence (Covidence, Melbourne, Australia).
Background: Environmental exposures in early life influence the development of behavioral outcomes in children, but research has not considered multiple exposures. We therefore aimed to investigate the impact of a broad spectrum of pre-and postnatal environmental exposures on child behavior. Methods and findings: We used data from the HELIX (Human Early Life Exposome) project, which was based on six longitudinal population-based birth cohorts in Europe. At 6-11 years, children underwent a follow-up to characterize their exposures and assess behavioral problems. We measured 88 prenatal and 123 childhood environmental factors, including outdoor, indoor, chemical, lifestyle and social exposures. Parent-reported behavioral problems included (1) internalizing, (2) externalizing scores, using the child behavior checklist (CBCL), and (3) the Conner's Attention Deficit Hyperactivity Disorder (ADHD) index, all outcomes being discrete raw counts.
Statistical analysis Exposure variables were transformed to achieve normality; when normality could not be achieved with a transformation, the variable was categorized, and then standardized by the inter-quartile range (IQR)  (Hernandez-Ferrer et al. , 2019). Missing exposure and covariate data were imputed using chained equations  (White et al. , 2011)  with the mice package in R (van  Buuren and Groothuis-Oudshoorn, 2011). Each imputation models included between 5 and 25 variables selected based on their correlation to the variable to impute, their proportion of missing and the main health outcomes. (20 imputed datasets were created, see more details in Annex 2). Missing values, ranged from no missing values for some child phthalate metabolites to 65% for fast-food intake during pregnancy (Table  A2.
1 in  Tamayo-Uria et al. , 2019). The mean percentage of missing values per exposure was 12% (first quartile 0. 9% and third quartile 16. 8%). None of the participants had complete data on all exposures. Yet, for 98% of individuals < 30% of exposure variables had missing values. Results of the complete case analysis are also available in Tables  A4. 2 and A4. 3 with the number of complete cases for each exposure before imputation. In all subsequent regression models, Rubin's rules were used to combine the results from the 20 imputed datasets, which allows the quantification of the uncertainty in results associated with imputation in the final standard errors, confidence intervals and p-values  (White et al. , 2011). All analyses were based on negative binomial regression with adjustment for cohort, maternal age, child age, child gender, season of conception and maternal education.
We also found that increased residential car traffic density on the nearest road during pregnancy, was associated with higher externalizing and ADHD scores. This exposure might represent an umbrella of other factors based on its correlation with other exposures in our dataset such as outdoor air pollutants (PM absorbance: r(spearman) = 0. 4) and traffic related noise level (r = 0. 17). Previous studies have demonstrated that traffic-related air pollution was associated with child neurobehavior such as inattention and higher prevalence of ADHD and Autism Spectrum Disorder  (Suades-González et al. , 2015). Traffic-related airborne particulate matter (PM) is a complex and heterogeneous mixture that includes residues from fossil fuel combustion, organic chemicals, trace metals, nitrate, and sulphate.
Strengths and limitations This study used standardized outcome and exposure measurement methods across six different countries with a wide range of exposure measurement techniques such as geographic information systems and satellite data for collecting outdoor exposure (road traffic, meteorological factors, natural spaces) and sensors (indoor air pollution). These exposure assessments were completed by more traditional methods such as standardized questionnaires for lifestyle and social capital variables and targeted biomonitoring for selected known (e. g. heavy metals and OP pesticides) and less known neurotoxic chemicals (e. g. phthalates and parabens). In addition, this prospective birth cohort design allowed the collection of objective prenatal exposure measures free of recall and response biases.
Acknowledgement ISGlobal acknowledges support from the  Spanish Ministry of Science and Innovation through the "Centro de Excelencia Severo Ochoa   2019-2023 "  Program  ( CEX2018-000806-S) , and support from the  Generalitat de Catalunya  through the  CERCA Program. We are grateful to all the participating children, parents, practitioners and researchers in the six countries who took part in this study. We further thank  Muireann Coen ,  Sonia Brishoual ,  Angelique Serre ,  Michele Grosdenier ,  Prof Frederic Millot ,  Elodie Migault ,  Manuela Boue ,  Sandy Bertin ,  Veronique Ferrand-Rigalleau ,  Céline Leger ,  Noella Gorry ,  Silvia Fochs ,  Nuria Pey ,  Cecilia Persavente ,  Susana Gros ,  Georgia Chalkiadaki ,  Danai Feida ,  Eirini Michalaki ,  Mariza Kampouri ,  Anny Kyriklaki ,  Minas Iakovidis ,  Maria Fasoulaki ,  Ingvild Essén ,  Heidi Marie Nordheim , and the  Yorkshire Water.
Funding/support The  LIFE-CYCLE  project has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No  733206. This study has been funded by  Instituto de Salud Carlos III  through the projects " CP14/00108 & PI16/00261 " (Co-funded by  European Regional Development Fund  "A way to make Europe"). LM is funded by a  Juan de la Cierva-Incorporación fellowship  ( IJC2018-035394-I ) awarded by the  Spanish Ministerio de Economía, Industria y Competitividad. JJ hold a Miguel Servet contract ( MS14/00108 ) awarded by the  Spanish Institute of Health Carlos III (Ministry of Economy and Competitiveness). The research leading to these results has received funding from the  European Community 's  Seventh Framework Programme  ( FP7/2007-206 ) under grant agreement no 308333 -the  HELIX  project.
CW received funding from the  Fondation de France  ( 00069251 , France). MC received funding from  Instituto de Salud Carlos III (Ministry of Economy and Competitiveness)  ( MS16/00128 ). INMA data collections were supported by grants from the  Instituto de Salud Carlos III, CIBERESP , and the  Generalitat de Catalunya-CIRIT (Spain). KANC was funded by the grant of the  Lithuanian Agency for Science Innovation and Technology  ( 6-04-2014_31V-66 ). The Norwegian Mother, Father and Child Cohort Study is supported by the  Norwegian Ministry of Health and Care Services  and the  Ministry of Education and Research. The Rhea project was financially supported by European projects, and the  Greek Ministry of Health  ( Program of Prevention  of obesity and neurodevelopmental disorders in preschool children, in Heraklion district, Crete, Greece: 2011-2014; " Rhea Plus": Primary Prevention Program of Environmental Risk Factors for Reproductive Health , and Child Health:  2012-15 ).
The work was also supported by  MICINN  [ MTM2015-68140-R]  and  Centro Nacional de Genotipado-CEGEN-PRB2-ISCIII (Spain). This paper presents independent research funded by the  National Institute for Health Research (NIHR)  under its  Collaboration for Applied Health Research and Care (CLAHRC) for Yorkshire and Humber (UK). Core support for Born in Bradford is also provided by the  Wellcome Trust  ( WT101597MA, UK ). Project " PI16/00118 ", funded by  Instituto de Salud Carlos III  and co-funded by  European Union (ERDF)  "A way to make Europe". This study has been funded by  Instituto de Salud Carlos III  through the projects " CP14/00108  and  PI16/00261 " (Co-funded by  European Regional Development Fund  "A way to make Europe"). Role of the Funder/Sponsor: The funding sources had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.
Materials and Methods This study was conducted by an international consortium of research centres in collaboration with the Queensland Mental Health Commission. The analysis of the impact of the Ed-LinQ initiative followed an ecological approach and used mixed methods with the collection of both qualitative and quantitative data to acquire organisational learning from a systems perspective, both for a public agency (lessons learned in the early implementation of the Ed-LINQ initiative) and international consortium (lessons learned to improve the impact analytics of the policy interventions)  [22]. Research methods included surveys, documentation review (workforce development reports, Ed-LinQ coordinator reports and implementation plans), focus groups, collaboration and joint meetings with stakeholders in targeted regions (Mackay, Gold Coast and Sunshine Coast) plus in-depth interviews with Ed-LinQ coordinators and the evaluation project reference group member (representatives from government, independent and catholic education sectors).
A checklist for the evaluation of adoption was included in the two surveys. The questionnaire also included questions related to the perception of the initiative by the respondent, such as the overall satisfaction, the perceived improvement of the staff's knowledge, students' access to mental health resources, students' attendance, and students' performance. The schools that were rated as "no adoption" (level of impact of 0) were not included in the analysis. The responses between those with high and low adoption were then compared in order to know what factors were associated with achieving a higher impact using bivariate Pearson's chi-squared tests (X 2 ): statistical significance level was set at 0. 05. Statistical analysis was undertaken using STATA SE 12 software. We followed the Standards for Reporting Implementation Studies (StaRI) checklist (Supplementary File 3) to format this manuscript  [28].
Impact on the Health Sector (Survey 2) (Adoption) The adoption of the Ed-LinQ initiative in the health sector was assessed in a survey addressed to CYMHS and other mental health and related services. A total of 78 professionals from health services across Queensland answered the survey. Seventy (90%) provided valid and complete data that were included in the analysis, reflecting a high level of interest in the Ed-LinQ program. Among the staff who answered the survey, the majority were clinicians (39%), team leaders or directors (26%) and case managers (18%). Most of them had extensive experience as nearly half (48%) had been working in health services for more than ten years and only 8% for less than two years. Among the professionals who completed Survey 2 (n = 70), 87% were at least aware of the Ed-LinQ initiative and 79% provided services, interventions and/or technologies directly related to the objectives of the Ed-LinQ initiative (Figure  4 ).
The adoption of the Ed-LinQ initiative in the health sector was assessed in a surve addressed to CYMHS and other mental health and related services. A total of 78 profe sionals from health services across Queensland answered the survey. Seventy (90%) pro vided valid and complete data that were included in the analysis, reflecting a high lev of interest in the Ed-LinQ program. Among the staff who answered the survey, the ma jority were clinicians (39%), team leaders or directors (26%) and case managers (18% Most of them had extensive experience as nearly half (48%) had been working in healt services for more than ten years and only 8% for less than two years. Among the professionals who completed Survey 2 (n = 70), 87% were at least awar of the Ed-LinQ initiative and 79% provided services, interventions and/or technologie directly related to the objectives of the Ed-LinQ initiative (Figure  4 ).
Our recommendations to the Queensland Mental Health Commission and Queensland Health included the following: the development of formalised governance arrangements and guidance at the state, region and local levels, so that the key stakeholders' roles and responsibilities are aligned and complementary; the improvement of data collection, analysis and reporting, in particular in terms of consistency, to ensure that services target the schools that are the most in need; that the best practice is identified and that the impact of the initiative is understood and quantified to inform future actions; and the clarification of the role of the Ed-LinQ coordinator and the establishment of a state-wide Ed-LinQ coordinator network, to provide coordinators with the opportunity to share resources, identify best practices and undertake joint strategic research or pilots.
Acknowledgments: This study was led by  ConNetica  with the collaboration of the  Mental Health Policy Unit, Brain and Mind Centre, University of Sydney  and the  Australian National University , and ECEVE research unit-UMR1123-INSERM and université  Paris Diderot (France). The study was funded by the  Queensland Mental Health Commission  and the 2016 research mobility grant by the  University of Sydney  and INSERM.
b. Settings Hospitals were either teaching or non-teaching, from private or public sectors. We contacted hospital directors and Institutional Review Boards (IRB) to grant us access to the hospital financial, clinical, and microbiological database. Hospitals are enrolled based on the location and their willingness to share financial data. The majority were reluctant. Each hospital gave its ethical approval before proceeding to data collection. Patient consent is not obtained as data were collected and analyzed anonymously. The study started in January 2016 and ended in December 2017.
Cost estimation The sources of costs are the hospital's financial database. Data included detailed total hospital stay charges, pharmacy expenditures, antibiotics charges, medical accessories, and serums, laboratory and pathology laboratory charges, imaging charges, Mechanical ventilation, thirdparty payer reimbursement, patient co-payment, and total hospitalization charges. All costs were expressed in Lebanese pounds and converted to US dollar currency (1US dollar =1507. 5 Lebanese Lira L. L). Total hospitalization costs may include infection prevention and control costs that were not provided by the hospitals in detail. The cost of hospitalization per patient case is the product of the billed charges during the hospitalization. Each case is assigned a number generated by the institutional database.
g. Statistical analysis For data entry, we used the Statistical Package for Social Sciences (SPSS) version 23. 0 software (SPSS Inc. , Chicago, IL, USA). We conducted a separate analysis for community-associated infections and healthcare-associated infections due to antibiotic-resistant versus -susceptible bacterial isolates. For CAIs, we used the conventional method and accounted for the LOS, hospital discharge, and in-hospital mortality for the entire hospital stay from admission to patient discharge from the hospital. We performed two different analyses for cases with HAIs and adjusted for the LOS, hospital charges, and mortality incurred post-infection: (1) Postinfection HAIs where we accounted in terms of hospital charges and LOS for the time from infection as defined by the date of the first culture till discharge from the hospital alive or dead and (2) matched analysis to minimize the risk of time-dependent bias where patients were matched at the analysis stage based on age, gender, Charlson comorbidity index, and on time to infection using the propensity score matching method.
Acknowledgments We would like to thank each and every person who gave us time and support and granted us access to clinical and economic data in all 10 hospitals throughout Lebanon. We highly value the trust of the hospital management and Institutional board review members and we thank all the colleagues and staff working in the  infection disease department, pharmacy department, laboratory department , accounting department and quality department in the following hospitals:  Lebanese Canadian Hospital, Horch Tabet; Middle East Institute of Health University Hospital, Mount Lebanon; Geitaoui Hospital, Beirut; Rafic Hariri University hospital, Beirut; Mounla Hospital, Tripoli ,  North Lebanon ;  Dar El Amal University Hospital, Baalbeck; American University Medical center, Rizk hospital, Beirut ;  Haroun Hospital ,  Mount Lebanon ;  Nabatieh Governmental hospital, Nabatieh ,  South Lebanon ;  Centre hospitalier du Nord, Zgharta ,  North Lebanon.
Methods: Data The present research used data from Building a Knowledge Base on Population Aging in India (BKPAI), a national-level survey conducted in 2011 across seven states of India. The survey was sponsored by the Institute for Social and Economic Change (ISEC), Tata Institute for Social Sciences (TISS), Institute for Economic Growth (IEG), and United Nations Population Fund (UNFPA), New Delhi. The survey gathered information on various socio-economic and health aspects of ageing among those aged 60 years and above. Seven major regionally representative states were selected for the survey with the highest 60+ years population than the national average. This survey was carried out on a representative sample in India's northern, western, eastern, and southern parts following a random sampling process.
Strengths and Limitations of the Study: One of this study's main limitations is the self-reporting of data related to ADL, IADL, and impairments. Previous studies also assessed these measures as per the self-reporting of the respondents  [11]. Furthermore, the study used chronic disease and self-rated health as two independent variables. These measures were also self-reported and may have biasness. Previous studies also used a self-reported measure of chronic disease and self-rated health as measuring chronic disease clinically may not be feasible  [11]. Furthermore, due to the cross-sectional study, we could not identify a causal relationship. Despite various limitations, this study has quite a few strengths that make this study unique. At rst, this study examined various factors associated with impairments along with ADL and IADL, thus, adding one extra dimension to the study related to ADL and IADL.