dataesr/scientific-paragraphs-categorization huggingface dataset:
1  The Rome Foundation Global Epidemiology Study (RFGES) on the prevalence and burden of the disorders of gut-brain interaction (DGBI), conducted from 2017 to 2020, was designed to rectify the shortcomings of prior DGBI population research by applying comparable methodology and identical measures in a large cohort of countries around the globe. The overall DGBI prevalence findings of that study were published in 2021. 2  The RFGES was carried out in 33 countries on six continents by an international team of experienced researchers on these disorders. In most countries, it was possible to collect the data by Internet survey. However, to attain a more complete global picture of the prevalence of DGBI, it was important to include countries in Africa, Asia, and the Middle East where Internet use is variably prevalent or where sociopolitical factors prevent Internet-based population surveying.
Key points • There is scant knowledge on the effect of data collection methods on the results of epidemiological studies. The Rome Foundation Global Epidemiology provided an opportunity to assess this, especially in China and Turkey where data were collected by Internet and inperson interviews. • Disorders of gut-brain interaction were only half as prevalent with in-person interview compared to Internet survey, including China and Turkey. • The choice of survey method has implications for epidemiological research in general and for DGBI research in particular. The present paper describes the design and implementation of the study, and most importantly, lessons learned about the impact of the specific survey methods chosen on quality of data and outcomes from the data collection.
| Data collection Data in the RFGES were collected by Internet-based surveys in 26 countries (Argentina, Australia, Belgium, Brazil, Canada, China, Colombia, Egypt, France, Germany, Holland, Israel, Italy, Japan, Mexico, Poland, Romania, Russia, Singapore, South Africa, South Korea, Spain, Sweden, Turkey, the UK, and the United States) and by in-person (face-to-face) household surveys in seven countries where it was not feasible to conduct an Internet survey either because of variably inadequate infrastructure (Ghana, Nigeria, India, Bangladesh, Malaysia, Indonesia), or because the population was not accessible through nationwide Internet surveying (Iran). As little is known from previous research about the relative quality of traditional in-person interviewing versus Internet surveying for epidemiological purposes, we decided at the planning stage of the study to test this directly.
Potential study participants were invited via e-mail in multiple waves until all quota categories were filled. The survey participants were entirely anonymous to the RFGES investigators, as the data were collected in a fully de-identified manner by design. The online RFGES survey contained multiple built-in quality assurance methods designed to ensure that the final samples consisted of valid and high-quality responses. Responses were enforced page by page, not allowing survey-takers to proceed without first completing missing answers, thereby preventing missing responses in the dataset. The numerous skip patterns were executed automatically. The Internet surveys were designed in such a way that they could be completed on mobile devices (mobile phone optimized survey formatting) as well as on computers or tablets.
Survey-takers who failed either of two attention-check questions showed inconsistent responding on either of two repeated questions about the frequency of specific gastrointestinal symptoms, or completed the survey excessively fast (i. e. , in less than 1/3 of median completion time for each country) were identified as invalid responders. Those invalid responders were deleted from the online datasets while surveying was ongoing and replaced by other participants from the same demographic quota groups. Data collection was completed in less than 1 month in each country surveyed through the Internet.
The methodology for selecting survey participants was predetermined for each country. In general, it was based on multi-stage sampling. In most countries, the investigators selected study areas in several distinct geographic regions. For example, China selected one northern city, one central province, and one southern province as study regions. Next, urban and rural community sites within the chosen regions were selected in most countries, based on convenience and practicality for data collection and their demographic composition. Finally, households in a particular street or direction in a neighborhood, or specific apartments in apartment buildings in the chosen communities were selected. Then, only one survey participant was selected per household for surveying, with an alternative individual in the same household listed as a potential substitute participant if the first person selected was unavailable or declined participation.
This rendered the accuracy of responses in the entire resulting dataset for Ghana questionable. Data from that country are therefore not included in any DGBI prevalence estimates presented in this paper. Data were collected in paper survey booklets in all countries except in Turkey, where Internet-connected mobile devices were used by the interviewers to display the questions and to enter the survey responses. Responses from the paper booklets were entered in a de-identified manner through an online data entry web form into the central study database by study staff. For quality assurance, interviewers administering the surveys were trained to review study booklets before finishing interviews and to complete missing answers with the participants whenever possible. The household surveys generally required a year or more of preparation, including design, planning and identification of survey locations, staff recruitment and training, and printing and distribution of survey booklets and other materials.
| Data entry quality checks for household surveys Because of the vast amount of manual data recording involved in data collection from paper-based personal interviews, which inevitably  entails some errors, we evaluated several aspects of data accuracy in the eight survey countries that used this data collection method. To enable assessment of the accuracy of data upload into the computer dataset, the study staff in each country was required to enter the entire datasets for a random sample of either 5% or 10% of all survey participants from their countries twice from the paper forms into the electronic database, and the entered values were then compared.
| Comparison of data entry quality and missing and inappropriately present data between the data collection methods As seen in Table  2 , based on the partial double entry exercise in the eight countries that used paper surveys, the overall data entry error rate for the countries ranged from 0. 3% (China) to 3. 9% (Ghana), with a median of 1. 6%. We also quantified missing answers and inappropriately included data in the datasets of these eight countries. For this, we limited the assessment to the 89-question Rome Diagnostic Questionnaire, which provided the most essential data for the purposes of the study. This questionnaire consists of stem questions about the presence and frequency of different gastrointestinal symptoms, required to be answered by all survey participants, as well as follow-up questions about further characteristics of each present symptom, which are intended to be skipped by survey respondents who report the symptom in question as absent.
| Comparisons of overall prevalence rates of DGBI between the two data collection methods As the primary purpose of the RFGES survey was to assess the prevalence of DGBI in the global population, we examined how the prevalence rates compared between the two survey methods. It should be noted that we did not include the Ghana dataset in DGBI prevalence calculations for household countries, due to a combination of indications of inadequate quality of data collection from that country; mainly the administration of the survey in multiple languages without use of professionally translated questionnaires, but also because of the high data entry error rate and an excessive rate of missing data in that country's dataset (Table  2 ). The overall pooled prevalence rates of DGBI (pooling conducted according to the meta-prevalence method of Yang 8 ) were substantially lower overall in the household survey countries compared to the Internet survey countries, generally at about half of the Internet survey prevalence (Table  3 ).
Gastrointestinal symptoms are socially sensitive topics, and in-person interviews could lead to underreporting of GI symptoms, as they may be perceived as intimate and embarrassing, leading to a reluctance to report them. Study participants are probably more comfortable reporting GI symptoms such as diarrhea, belching, or fecal incontinence in an anonymous Internet survey. A clinical study showed reticence in reporting fecal incontinence in the clinical setting, naming it the "unvoiced symptom. "  10  In the absence of other identifiable factors, we believe that this sensitivity is likely to affect symptom reporting in GI in-person data collection, compared to anonymous reporting in a private setting on the Internet. In support of this, we note that the prevalence difference in household versus Internet surveys in our results was the highest for bowel and anorectal disorders.
Cultural differences, beliefs, and norms can lead people in different populations to interpret and report symptoms differently. 12 An alternative approach to text-based questionnaires is the use of pictograms, at least for some questionnaire items, with the intent of making these questions more culturally relevant. 13 Several studies have shown that the concept of "constipation" has different meanings in Asia and Western countries. 14 In India, for example, vegetarian diets can lead to children having several bowel movements daily. If the diet is changed and there is only one bowel movement per day, this may be interpreted and reported as constipation. However, the accepted diagnostic criteria would not lead to a diagnosis of abnormal bowel function in either of these cases. The use of both data collection methods in China and Turkey enabled us to get a better picture of this issue, especially in Turkey, where the study populations surveyed with the two methods were very similar.
The findings have important implications for population-based GI research, and research in other population health domains as well. They demonstrate that the methodology selected affects not only the quality of the data in the dataset but can also substantially influence the actual findings and conclusions regarding the health parameters investigated. The two survey methodologies implemented in the RFGES are the two main options left to investigators in the present era of research. Other methods that were commonly used in earlier research, such as telephone and mail surveys, have become increasingly impractical or unusable. With landlines becoming rare in many countries, much of the adult population has become unreachable by phone as they screen their mobile phone calls and do not answer phone calls from unknown callers.
Analysis of missing and inappropriately present data was limited to the 89-question Rome Diagnostic Questionnaire in the survey. a Stem questions were about the presence of gastrointestinal symptoms, with response options on a multiple-choice frequency scale. They were intended to be answered by all respondents. Assessment of data quality in household survey countries. Data entry error rate was based on either 5% or 10% manual re-entry of survey data into the electronic database.
6 | 6 The findings have important implications for population-based GI research, and research in other population health domains as well. They demonstrate that the methodology selected affects not only the quality of the data in the dataset but can also substantially influence the actual findings and conclusions regarding the health parameters investigated. The two survey methodologies implemented in the RFGES are the two main options left to investigators in the present era of research. Other methods that were commonly used in earlier research, such as telephone and mail surveys, have become increasingly impractical or unusable. With landlines becoming rare in many countries, much of the adult population has become unreachable by phone as they screen their mobile phone calls and do not answer phone calls from unknown callers.
Data entry error rate Missing stem question responses a Missing follow-up question (inside skip- patterns) responses b Inappropriately present follow-up questions (should have been skipped) Analysis of missing and inappropriately present data was limited to the 89-question Rome Diagnostic Questionnaire in the survey. a Stem questions were about the presence of gastrointestinal symptoms, with response options on a multiple-choice frequency scale. They were intended to be answered by all respondents. Assessment of data quality in household survey countries. Data entry error rate was based on either 5% or 10% manual re-entry of survey data into the electronic database. Bangladesh 0. 7% 0. 3% 1. 1% 0. 1% China 0. 3% 0. 4% 0. 9% 0. 0% Ghana 3. 9% 3. 1% 32. 5% 0. 5% India 0. 5% 0. 2% 1. 9% 0. 1% Indonesia 3.
Background: Considered the most prevalent noncommunicable disease in childhood, dental caries is both an individual and a collective burden. While international guidelines highlight prevention as a major strategy for caries management in children, health professionals still struggle to implement prevention into their clinical practice. Further research is needed to understand the gap between the theoretical signi cance of dental prevention and its lack of implementation in the clinical setting. This systematic review aims to identify and classify factors perceived by health professionals to be barriers or facilitators to caries prevention in children. Method: A systematic literature search was conducted in three electronic databases (Medline, Web of Science and Cairn). Two researchers independently screened titles, abstracts and texts.
To be selected, studies had to focus on barriers or facilitators to caries prevention in children and include health professionals as study participants. Qualitative and quantitative studies were selected. The factors in uencing caries prevention in children were sorted into 3 main categories (clinician-related factors, patient-related factors, and organizational-related factors) and then classi ed according to the 14 domains of the theoretical domains framework (TDF). Results: A total of 1771 references were found by combining manual and database searches. Among them, 26 studies met the inclusion criteria, of which half were qualitative and half were quantitative studies. Dentists (n=12), pediatricians (n= 11), nurses (n=9), and physicians (n=5) were the most frequently interviewed health professionals in our analysis.
Searches The search strategy was designed in collaboration with a medical librarian. The following three electronic databases were searched: MEDLINE, Web of Science and Cairn. MeSH terms were used on Medline, and free text terms were used on Web of Science and Cairn (see Additional File 2). The search was conducted with no initial time restriction to March 2021 for French and English papers only. No search of gray literature was undertaken. The collection was completed with hand searches of the reference lists of all selected studies.
Data extraction strategy Data were extracted using the same method as for the study selection. Full texts were analyzed independently by two researchers (GL and EM) with arbitration by a third team member (MC) in case of disagreement. The data collection template included the year of publication, country of the study, primary and secondary objectives of the study, study design, sample size, profession of respondents and main factors identi ed as barriers or facilitators to caries prevention. For quantitative studies, a factor was considered a barrier when at least 10% of participants reported it as such. This threshold value was decided by consensus of the research team members who considered 10% to be a population-wide signi cant portion. For qualitative studies, data were collected from participant quotations.
Study selection and characteristics The database research found 1768 references (Fig. 1 ). After duplicates were removed, 1710 studies were screened based on the title and abstract. Of these, 31 were selected for full text examination. After hand searches of the reference lists of all selected studies, 3 additional articles were selected. Ultimately, full text screening was conducted for 34 studies. Eight were excluded (see Additional File 3), and 26 met the inclusion criteria, among which half were qualitative studies  (30, (37) (38) (39) (40) (41) (42) (43) (44) (45) (46) (47) (48)  and half were quantitative studies  (24, 25, (49) (50) (51) (52) (53) (54) (55) (56) (57) (58) (59). Methodological quality assessment did not lead to further exclusion of any references. Among the included papers, 25 were reported in English and one in French (46).
Methodological quality For the qualitative studies retained, quality assessment using the COREQ checklist showed signi cant differences in terms of methodological quality (Additional le 4). Overall, the studies provided su cient detail on aims, the participant selection process, data analysis and reporting. In contrast, more than half of the studies provided poor or no information on the use of a theoretical framework, interviewers' characteristics, the relationship between the research team and the participants and data saturation. The assessment of quantitative studies showed good methodological quality since most of the items from the STROBE checklist were mentioned for all studies (Additional le 5).
1 , which is composed of three main components: data collection, The platform's recommendations will heavily rely on the data it collects. The primary sources of this data will be the citizen's self-evaluation survey responses and connected devices. The survey's questions are chosen from different frailty surveys. They were selected based on whether the citizen can answer them alone (for example, ADL 1  questions) and whether they provide an understanding of the user's condition. Additionally, service providers are responsible for defining their services on the platform, which is the second input for the recommendation engine. The collected data will feed the recommendation engine, which will present the citizen with a list of services that meet their needs, factoring in their personal characteristics and preferences.
For example, the goal of "establishing new friendships" can be associated with the category "Social relationships" which is linked to psychological capacity. -Activity Preferences: This section provides more details on the person's activity preferences and their respective category. -Recommendation Preferences: Helps the recommendation engine to filter the recommended services based on the person's preferences, for example, by specifying a preference for well-rated services. -Personal Characteristics: Are obtained from responses to the survey and can be classified into different categories of IC. For example, the characteristic "Vision problem" can be identified from the "Comprehensive Geriatric Assessment" survey  [11]  and is associated to the visual capacity  [10]. Although our platform can generate an estimated frailty score based on the data collected, it's essential to acknowledge that this score may not be as accurate as those obtained by healthcare professionals.
Recommender Engine The Senselife platform employs a recommendation engine that provides services for frailty prevention. With the help of algorithms, this recommendation engine suggests specific services to elderly individuals based on their personal information and circumstances. The engine evaluates the data provided by the elderly person and suggests the most relevant services using the knowledge bases and the rule base available. The knowledge base contains the different personal characteristics, needs, the service types and the relation between them. The rule base contains the rules that will used to manipulate the in the knowledge base. The validation of this rules are based the expertise of the health-care professionals. There are various recommendation techniques, such as collaborative filtering algorithms, content-based algorithms, and knowledge-based algorithms.
Data analysis Data was analyzed using an inductive, bottom-up approach  [37]. A qualitative thematic analysis  [37] , following the steps of  Braun & Clarke (2006)  was applied to the interviews using Nvivo12. All focus group interviews were audiotaped and written into transcripts by two independent researchers. In the first phase, before the coding process, the researchers familiarized themselves with the data of the transcripts by repeated reading of the data. Transcripts were not returned to participants for questions or comments. In the second step, the initial codes were generated. To maximize reliability, this was done separately by two researchers (Interviews in Dutch: MD (child & adolescent psychologist, PhD student, female) and SF (Cognitive neuroscientist and obesity expert, PhD, female); interviews in French: AF (obesity therapist, PhD, female) and DT (physiologist, Prof.
Objective: We aimed to review the current published literature to identify different stakeholders' perspectives and recommendations on what information patients and members of the public want to learn about the secondary use of health data for research purposes and how and in which situations. Methods: Using PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines, we conducted a scoping review using Medline, CINAHL, PsycINFO, Scopus, Cochrane Library, and PubMed databases to locate a broad range of studies published in English or French until November 2022. We included articles reporting a stakeholder's perspective or recommendations of what information patients and members of the public want to learn about the secondary use of health data for research purposes and how or in which situations.
Search Strategy An extensive literature search was performed for articles up to November 2022 in the following bibliographic databases: Medline, CINAHL, PsycINFO, Scopus, Cochrane Library, and PubMed. The search strategy was developed with the assistance of a university librarian and was adapted to each database (the detailed search strategies are provided in Multimedia Appendix 2). The 5 core concepts used in the search strategy were patient, public, citizen (and synonyms); health data, medical data, data use, data sharing (and synonyms); attitude, view, perspective, opinion, position (and synonyms); information, transparency, communication, dissemination, awareness, notification, education (and synonyms); and research, secondary use, LHS (and synonyms). We did not have to limit the time span.
Data Extraction and Analysis The extraction, organization, and analysis of information from the articles included in this scoping review was performed using NVivo 11 Pro software (QSR International), a tool to conduct extensive literature reviews  [29]. On the basis of the aforementioned research questions and objectives, the research team developed a codebook with an initial tree of nodes to chart the data into NVivo. This codebook also includes the coding procedure and definitions of the nodes for the reviewers responsible for the data extraction. All selected articles were imported into NVivo software. Four reviewers (RD, FL, AC, and 1 research assistant) coded all the articles, a process that was subsequently validated by 2 reviewers (AC and J-FM). All reviewers received detailed training in the software, project, and codebook.