dataesr/scientific-paragraphs-categorization huggingface dataset:
Study population and design All the data came retrospectively from the program described above and called "Neuchâtel s'attaque au surpoids" ("Neuchâtel tackles overweight"). It is why the criteria for participation, inclusion, and exclusion are by extension similar to those of the studied program. The program ran from September 5 to December 5, 2021. The anthropomorphic data and sociodemographic profiles of the participants at the start and at the end of the program, as well as the QoL PROMs were available. Only the participants who consented to the use of their data for scientific purposes were examined. We assessed changes in QoL, as well as weight loss and waist circumference between the beginning and the end of the program. For this step, only data from participants who entirely provided the data and completed questionnaires were considered.
The statistical significance threshold was set at p ≤ 0. 05. Afterwards, we calculated whether the evolution of the QoL varied according to anthropomorphic data and sociodemographic profiles registered at the start of the program. For the QoL domains which showed significant improvement, a multivariate analysis was performed. The following parameters were considered: age and baseline BMI as linear parameters, followed by gender, family status (living as a couple or being single), persons with or without children, educational level (primary versus secondary or high school), and occupation (employment compared to unemployment or any kind of social assistance), as well as country of birth (Switzerland versus another country) as binary parameters.
Acknowledgments The authors would like to thank  Basma Benhaddou  and  Guy Temporal  for their assistance with proofreading this article,  Sanja Blazevic  and  Christine Reuse , community managers, for their dedicated work,  Sabrina Alberti , a psychologist for her participation in the program. Special thanks go to  Pascale Giron-Lanctuit , cofounder of Pour un Poids Sain (PoPs) for her invaluable support. Special thanks also go to the regretted  Roland Savioz , owner of ObeeOne, who initiated the idea of the program, and to  Baptiste Savioz , his son, who provided the concept to the non-profit organizations (NPO). Huge thanks go to the members.
Randomization The randomization will be performed in a 3:1 (experimental: control) ratio into two groups, namely (1) the experimental group, who will participate in an APA program; and (2) a control group, who will not participate in the APA program. This study will be performed into two centers (University Hospital of Besancon and the North Franche-ComtéHospital, France) and recruitment will be competitive. Randomization and data management will be performed using the CleanWeb business application (version 175. 3. 0), an electronic clinical trial management solution from Telemedicine Technologies (Boulogne-Billancourt, France).
This group will allow to estimate the "natural" progression of the primary outcome without any APA program Conduct of the study. (control); Follow-up data (excluding activity assessment) will also be collected on this 10-patient group. Randomization will be performed in a 3:1 (experimental: control) ratio according to the design of the study. The study methodology and statistical analyses are managed by the research methods unit (uMETh) of the clinical investigation center, University Hospital of Besancon.
The number of meters walked on average per month will be presented graphically for each measurement time, in the experimental arm only. Average scores on the JADAS-27, CHAQ-38 (9 activity items evaluated and score of functional incapacity, pain, and the parent or patient global assessment of arthritis), Peds-QL (sub-score physical and psychological health) and PedsQL-MFS (sub-score sleep quality, general and cognitive fatigue) with standard deviations will be presented graphically for each measurement time (V0-V1-V2-V3) for each group, and also by age group (6-7 years, 8-12 years, 13-17 years) within each group. As an exploratory basis, if the data are amenable, univariable linear regression model will be used to test the variables potentially associated with progression of disease activity, physical activity, and quality of life (age, body mass index, number of swollen joints).
Digital Health Implementations and Interventions For this paper, intervention refers to the deliberate effort to change a situation or modify behavior, and implementation refers to the process of achieving the goal (how the intervention was carried out). Digital health is defined as the use of information and communication technologies for health. As with this definition, the scope is broad, and interventions can range from targeting an individual to strengthening components of health systems. Examples include tracking individual parameters, managing logistics for vaccine delivery, or conducting advanced data analytics for precision medicine. The World Health Organization's (WHO) Classification of Digital Health Interventions provides a good overview of the potential range of applications by organizing them into 4 umbrella groups based on the target primary user: (1) interventions for clients, (2) interventions for health care providers, (3) interventions for health system or resource managers, or (4) interventions for data services  [5].
Methodology The methodology outlined by Moher et al  [14]  was used to develop iCHECK-DH. To define the initial draft list of reporting items, we conducted a literature review that included searching multiple databases (PubMed, Google Scholar) for papers describing digital health implementations in different settings. Papers that focused on digital health technologies but did not discuss implementation were excluded. A set of 10 selected papers from different settings (eg, primary care, community care) were reviewed for common reporting elements as input for the first draft checklist. This initial draft checklist was developed by a team of 2 researchers and reviewed for completeness by a range of global stakeholders with different backgrounds and roles (researchers, implementers, technical experts).
Examples • "Chatbot-Based Assessment of Employees' Mental Health: Design Process and Pilot Implementation"  [18]  • "Creation and Global Deployment of a Mobile, Application-Based Cognitive Simulator for Cardiac Surgical Procedures"  [19]  • "The Journey to National Scale of Zanzibar's Digitally Enabled Community Health Program: An Implementation Report"  [15]  Explanation Authors should choose a title that is concise yet accurately describes the purpose of the document as an implementation report to ensure maximum visibility and easy access. Strategic use of keywords in the title can make this document searchable in various publication databases for optimal reach and machine processing, without the need to explicitly mention "implementation. ".
Example • "Background: Patient empowerment can be associated with better health outcomes, especially for chronic diseases. Concerto is a mobile application designed to promote patient empowerment in an in-patient setting. Methods: The application was designed and prototyped during a hackathon. It uses data from the hospital information system to provide key functionalities: a care plan, access to targeted medical information, practical information, information about the nursing team on duty, and a medical round preparation module. Following a feasibility study, funding was obtained, and the application developed using an agile methodology and deployed in 4 pilot divisions, using institution-owned iPads. Results: The project lasted for two years with effective implementation in the 4 pilot divisions, within budget.
The induced workload on caregivers was a key challenge that warranted a change in our implementation strategy. The presence of a killer-function would have facilitated the deployment. Furthermore, our experience is in line with the well-accepted need for both a high-quality user-training and a good selection and engagement of super-users. By presenting Health Information System data directly to the patient, Concerto has highlighted them to be not fit-for-purpose and has triggered data curation initiatives. Finally, connecting the application to the HIS has promoted the usage of standards, both on the HIS and mobile applications side, that should facilitate future initiatives. Discussion: This implementation report presents a real-world example of implementing a patient-empowering mobile application in an in-patient setting of a University Hospital.
Examples • "The Jamii ni Afya mobile app is built on the Community Health Toolkit (CHT), an open source global goods platform developed to support community health workers globally. This platform was selected by the Zanzibar government due to the following reasons: it is open-source and uses well-known components and frameworks; it has a growing community that can be leveraged for support; it can be hosted in a local datacenter; the skills required to configure health worker tools are found among Ministry ICT staff and easily available in the local market. In addition, CHT runs on low-end Android smartphones and has offline functionality, which is critical in Zanzibar where network connectivity is not guaranteed"  [15]. • "The application was developed in a web-based, responsive, coding language, encapsulated for iOS and deployed on institution-owned iPads using a mobile device management.
The key arguments for internal development over acquisition of a commercial solution were that (1) most of the development work was about interfacing with the Hospital Health Information System, (2) no mature commercial solution was available at that time, to our knowledge"  [16]. • "The Java programming language was used to develop a native app focusing on the Android platform, in which personal and medical data are maintained using the SQLite database. The first version of the app was con-structed and presented to the same nephrologist still in 2015. This aimed to make sure that all specified requirements were incorporated into the app"  [28].
Examples • "Concerto was mainly 'read-only' for personal health data present in the HIS and for unsensitive, unpersonal information. The information patients accessed from the HIS were part of its medical record. According to the Swiss law, every patient owns data from his medical record, except for personal notes of healthcare professionals which were out of the scope of concerto. Accordingly, concerto facilitated the access to data already owned by the patients. The only personal information entered in concerto was questions patients may have before interaction with caregivers. This information was stored locally on the iPad. As iPads were erased and reinitialised between patients, this information was systematically deleted. Secure login information based on the patient ID number and an SMS challenge also protected from unwanted access to sensitive personal information.
Overall, the project was compliant with the Swiss law for data protection"  [16]. • "The program consists of a clinic computer that hosts the local database of patient names and their phone numbers. A secure server, hosted by Sawubona Health, synchronizes with the local database. Each week, the server uses PHP scripts (commonly used for web development) to automatically generate the messages and pass them and the corresponding phone numbers to a BulkSMS service provider (Celerity Systems LTD, South Africa) for distribution"  [30].
• "The feasibility study and initial concept were self-funded by the eHealth and Telemedicine Division of the Geneva University Hospitals and included salaries for a junior developer and a senior project manager. The project pilot was then funded by the Fondation Privée des HUG and included the aforementioned salaries as well as necessary materials (in particular iPads, covers and software licenses). No direct state-funding was provided at that stage of the project"  [16].
Examples • "We conducted a randomized controlled pilot trial to assess the SELMA intervention. The clinical trial was approved by the Cantonal Ethics Committee of Zurich (KEK-ZH study protocol identifier Nr. 2017-02136) and was registered at the Swiss National Clinical Trial Portal (SNCTP000002712) and the WHO-accredited German Clinical Trials Register (DRKS00017147). Data protection requirements were fulfilled according to the KEK-ZH"  [27]. • "A randomized control trial assessing the effectiveness of the Concerto mobile application on a patient situation awareness score has been designed and should be conducted soon. It will allow for a better evaluation of the cost-effectiveness of such project. Overall, data on the effectiveness of eHealth projects are often lacking, and the creation of the JMIR implementation reports is aiming to fill that gap"  [16].
Acknowledgments We thank the  Swiss Agency for Development and Cooperation  and the  University of Geneva  for supporting the guideline development process through the  Geneva Digital Health Hub  (gdhub). We thank all those outside the guideline development group who reviewed the checklist, in particular  Mirana Randriambelonoro  and  Riccardo Lampariello. We are also grateful to the participants of the implementation reporting guideline workshop at the  Global Digital Health Forum  workshop in Washington in December 2022 and the Digital Health Conference in Kathmandu in November 2022.
Search strategy A systematic search of PubMed, Scopus and PsycINFO databases was conducted in April 2020. Literature reviews whose explicit aim included the following terms were targeted: stigma by healthcare professionals on people with addiction or addiction treatment. For this study, stigma was defined as negative attitudes or negative beliefs of healthcare professionals towards people with an addiction (i. e. , substance or behavior), addiction, addiction treatment, or a lesser involvement in treatment. The publication dates and language of reviews were not limited. The search strategy was the same for the three databases. Keywords were adapted to the specific thesaurus of each database (Table  1 ). To be considered a literature review, the publication had to have an explicit strategy for collecting the literature, visible in the title, abstract or full article.
Study selection After extraction from the databases and removing duplicates, the first selection was based on the title. The words 'people with addiction' or 'addiction treatment' (or equivalent), 'health professionals' (or equivalent) and 'stigma' (or equivalent) had to appear (Table  2 ). The following selections, from the abstract and then the full text, checked the aim of the review and that they were literature reviews. Articles dealing with the general attitude (negative or positive) of professionals towards people with an addiction were also included. The full text selection was done by two reviewers independently. Literature references for each included study were systematically checked for additional publications.
Quality assessment The quality of publications was assessed using the AMSTAR checklist  (Shea et al. , 2009). In particular, this tool assessed the process of literature search, study selection, data extraction, evaluation of the quality of studies, the method for combining results and publication bias. Each item was answered with: 'yes', 'no', 'cannot answer' or 'not applicable'. This scoring system was worth 1 point for 'yes' and 0 point for the other response options. Categories were determined as follows: low (score 0 to 3), medium (score 4 to 7), and high (score 8 to 11) (Canadian Agency for Drugs and Technologies in Health, 2011). All results were described regardless of quality, but only results from reviews with medium and high qualities were considered when drawing conclusions.
Data extraction Useful data were extracted and collected in a table for thematic analysis. First, the raw data was collected, then a closed checklist was constituted using distinct homogeneous categories. Each publication was then re-analyzed using this checklist. Data collected were synthesized to answer the study's aims. Based on the data collected, the synthesis focused on (1) stigma characteristics and prevalence (2) health professionals' characteristics (3) stigmatized patients' characteristics and (4) the evolution of stigma prevalence over time.
Stigma's characteristics and prevalence (Table 5) In this section, only eleven reviews that reported quantitative and/or qualitative prevalence were included. Data from reviews referring to attitudes, beliefs or vision of care without prevalence data were not included. Of these eleven reviews, three reported on the general attitudes of professionals  (Ducray and Pilch, 2017; Howard and Chung, 2000a; Van Boekel et al. , 2013). Ten reviews reported professionals' beliefs, including five on PWSUD  (Guydish et al. , 2007; Howard and Chung, 2000a; Sheals et al. , 2016; Van Boekel et al. , 2013; Howard and Chung, 2000b) , five on addiction  (Guydish et al. , 2007; Howard and Chung, 2000a , 2000b , 2000c; Sheals et al. , 2016)  and eight on treatment  (Flemming et al. , 2016; Guydish et al. , 2007; Howard and Chung, 2000a, 2000b; Rush et al.
Data collection and measurements Data collection included a comprehensive set of information on maternal and paternal characteristics, pregnancy, birth, and postnatal outcomes as well as follow-up data on the child's health, development, and environment. Data collection methods included a face-to-face interview conducted by trained investigators at the maternity hospital and computer-assisted telephone interviews conducted by a survey institute at the 2-month, 2-year and 3. 5-year follow-ups. In addition, information from the medical record, including the mother's health history and the child's anthropometric measurements and health status at birth, was collected at the maternity hospital. Information on the mother's SEP and migration history was collected during the 2-month follow-up. During the telephone follow-up interview at 3.
Statistical analyses We rst estimated the prevalence of children's overweight at 3. 5 years, before and after accounting for the study design and non-response at inclusion and at follow-up, using speci c weighting. This weighting method included calibration on margins from the state register's statistical data as well as data from the 2010 French National Perinatal study 18 for the following variables: sex, region, parity, marital status, mother's and father's age, mother's education, mother's migration status, mother's and father's employment at the time of delivery, child-birth preparation sessions, mother living in a partnership at birth, and alcohol consumption during pregnancy. This approach complies with the procedures recommended by the French National Institute of Statistics and Economic Studies.
Of 9,250 children included, 653 were classi ed as overweight: 7. 1% (95% CI 6. 5-7. 6) before data weighting and 8. 3% (7. 7-9. 0) after weighting (Table  1 ). The prevalence of overweight differed by regions, with the highest in Hauts-de-France (north) (10. 8%) and the lowest in Auvergne-Rhône-Alpes (middle east) (5. 1%; weighted prevalence) (Fig. 3 ). We found a social gradient in overweight for both migration and SEP (Fig. 4 ). The subgroups of children the most affected by excess weight were those born to immigrant mothers (weighted prevalence: ). Given that we found no interaction between migration status and SEP ((p > 0. 2), we did not stratify the analyses afterward. In the multivariable models, the probability of overweight status differed by migration status and was inversely associated with all three dimensions of SEP (.
Acknowledgements: We thank all investigators working on the EndObesity Project. This project received funding from the European Union's Horizon 2020 research and innovation program under the ERA-NET Cofund action (grant agreement no. 727565) and the European Joint Programming Initiative "A Healthy Diet for a Healthy Life" (JPI HDHL, EndObesity). We used variables that were harmonized in the framework of the H2020 LifeCycle project. The LifeCycle project received funding from the European Union's Horizon 2020 research and innovation program (grant agreement no. 733206 LifeCycle). The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. Research relating to this article was funded by the Pierre Louis doctoral school of Public Health.
16  To our knowledge, missing from this literature is the natural history of purpose in life along the continuum of dementia. Of particular interest is the timing of change in purpose across this continuum and whether it can be detected with self-report. For example, declines in purpose prior to cognitive impairment may be an early indicator of impending disease, and/or declines in purpose during cognitive impairment may be a consequence of the ongoing disease process. The 2 possibilities are not mutually exclusive. The present research used data from 2 large, population-based longitudinal studies to examine changes in purpose in life before and during cognitive impairment while accounting for normative changes. Importantly, with numerous assessments of purpose and cognitive impairment from the same individuals tracked over time, it was possible to differentiate change before the onset of cognitive impairment from change that occurred during cognitive impairment.
Participants and Procedure This cohort study used data from participants in the Health and Retirement Study (HRS) from March 2006 to May 2021  17  and the National Health and Aging Trends Study (NHATS) from May 2011 to November 2021. 18  A random half of the HRS sample first reported their purpose in life at the 2006 assessment; the other half of the HRS sample reported their purpose in life at the 2008 assessment. Participants in HRS subsequently reported their purpose at 4-year intervals through 2020 (the most recent assessment with available data). Participants in NHATS reported their purpose at each annual assessment from the first wave in 2011 to the 2021 assessment (the most recent assessment with available data). In both studies, participants were administered a cognitive battery used to assess cognitive status at each wave.
Statistical Analysis Multilevel modeling  23  was used to analyze the data because of the hierarchical structure of measurement occasions (level 1) nested within persons (level 2). We followed the same approach to modeling purpose over time as a previous study. 24  Specifically, the model included intercept and slope sample means as fixed effects and individual deviations from the means as random effects (ie, variation between persons on intercepts and slopes were included in the estimated models). The covariates were included as time-invariant variables. To capture normative change in purpose in life over time,  25  we coded time in years starting from the first assessment of purpose (eg, 0 for the first assessment, 0. 4 for a second assessment 4 years later in HRS, and 0. 1 for a second assessment 1 year later in NHATS); 1 unit corresponded to 1 decade for all time-related variables.
| Study design We conducted a prospective parallel-group open-label randomised controlled trial with inclusions from early October 2008 to late July 2012. The participating infants were randomly assigned before discharge to receive either the intervention or standard care at a 1:1 ratio. Randomisation was stratified by type of pregnancy, either single or multiple, and by gestational age at birth as follows: <26, 26-27, 28-29 weeks of gestation. The infants were allocated to groups after their parents provided consent. Twins were allocated to the same group due to the nature of the intervention. Randomisation lists were created by an independent statistician who was not involved in the study. For each stratum, the sequence of randomisation was generated with the Stata 11 software (StataCorp), using permuted blocks of varying size, with a minimum block size of two.