dataesr/scientific-paragraphs-categorization huggingface dataset:
These results contribute various streams of research using the task approach to assess the relationship between technological change and labour market outcomes  (ALM, 2003; Acemoglu and Autor, 2011). To begin with, our new database based on the combination of DOT and O*NET gives us the opportunity to analyse a time period that is both longer or more recent compared to previous studies on within-occupation task changes  (ALM, 2003; Spitz-Oener, 2006; Ross, 2017a,b). The most comprehensive study by  Spitz-Oener (2006)  focuses on Germany, but only until 1999 and without focusing on long-term employment growth. Our study also adds to prior work by  Lin (2011)  on new occupations and technology diffusion in US cities, with the main difference being that Lin tracks new job titles in the census classification based on the DOT over the period 1980-2000, while we study how the task content of both existing and new occupations evolves between 1980 and 2010.
The remainder of the paper is organized as follows. Section 2 details the main sources and the procedures for the construction of our DOT-ONET database. This is followed in Section 3 by a series of detailed descriptions of evidence on the evolution of within-occupation task changes over the period 1980-2010. Section 4 follows up on that and presents regression analyses, organized in three steps: the association between computer use at work and task configuration; changes in education supply associated with shifts in occupational task content; and employment outcomes by occupation and by macro-sectors. Section 5 summarizes and concludes.
Measure The key variables for our analysis are measures of occupational skill requirements and task intensity. Previous studies have relied on one of the two sources available for the US, namely, the 1977 and 1991 editions of  DOT (e. g. , ALM, 2003)  or O*NET (e. g. ,  Acemoglu and Autor, 2011). One of the main contributions of the present paper is the elaboration of a novel matching procedure to merge DOT and O*NET and the extension of the time horizon of the analysis. The main critical issue is that O*NET has a comparatively higher number of task-related variables (approximately 400) compared to DOT (44). Moreover, O*NET measures have different scales: the ordinal 'level' scale (0-7) and the ordinal 'importance' scale (1-5). 4 This is also recognised by  Autor (2013, p. 192) : "When the DOT was replaced by the O*NET in 1998, the complexity of the database increased by an order of magnitude.
Version 14. 0 of the O*NET database, released in June of 2009, contained 400 separate rating scales, which is almost half as many scales as the number of occupations coded by O*NET  […]  In practice, this means that researchers who wish to use these databases as sources for task measures are essentially required to pick and choose among the plethora of scales available, a problem that is much more severe for O*NET than for DOT. " [emphasis is our own]. Consequently, the task selection originally proposed by  ALM (2003)  is not suited to our purpose and, due to the constraints highlighted above (high dimensionality and plurality of scales in O*NET), some degree of freedom in the choice of the ideal task measures following researcher discretion is critical. 3 The occupational classification used in the two versions of DOT features about 12,000 occupations, while the occupational classification used in the two versions of O*NET that we use include, respectively, 900 (O*NET 4.
Subsequent versions of this publication do not contain explicit references to DOT. Accordingly, we thoroughly examined variable descriptions in both sources to search for suitable matches. To illustrate, we consider that the DOT variable Clerical Perception ("the ability to perceive pertinent detail in verbal or tabular material. Ability to observe differences in copy, to proofread words and numbers, and to avoid perceptual errors in arithmetic computation") bears a very similar title and description to the O*NET item Clerical ("knowledge of administrative and clerical procedures and systems such as word processing, managing files and records, stenography and transcription, designing forms, and other office procedures and terminology"). Our third general rule has been to maintain similar scales for task scores in the two databases.
Our first search yielded 16 different items, four for each of the dimensions of occupational task requirements that can be meaningfully associated between DOT and O*NET. 5 In a second iteration we further reduced the selection to four items (one per dimension) following the aforementioned three criteria. These four items have been subsequently used to build an occupational task intensity measure. Table  1  shows the DOT-O*NET matching items and reports the scale of each in the two data sources. When more than one candidate item was found in O*NET, we took the average value. To illustrate, we use the case of MATH and MANUAL. For two measures the scale is similar (MANUAL and CLERIC, with 1-5 level in DOT and 1-5 importance in O*NET), while it is different for MATH and LANGUAGE. 6 The discrepancy is due to the different range between levels in DOT and levels in O*NET (DOT scale of 1-6 vs O*NET 0-7).
Technological change and within-occupation task changes We examine the association between within-occupation task shifts and a proxy of technological change in the workplace: the change in the share of workers using computers. Although we are aware of the limitations of this measure, it is the only occupation-level measure for which data are available and that has been used in previous studies (e. g. ,  Autor, Katz and Krueger, 1998). As information on computer use at work by occupations from CPS (Current Population Survey) is only available for few selected samples, we focus our analysis on the 1990-2000 decade. 16 This is critical for our study given the data compatibility issues raised by the combination of DOT and O*NET. Similarly, within-occupation task changes occurred mostly in this decade, which further reinforces our focus on this decade.
A novelty of the present study is that, besides standard measures of skill upgrading such as the share of graduates working a specific occupation, we use data on the Classification of Instructional Program (CIP). These provide, for the entire timespan under analysis, an accurate description of the number and the types of instructional programmes that are required to perform the set of tasks of a given occupation. A key advantage of CIP data is that they are organized by SOC code and thus can be easily matched with our original data (see the Appendix C for details). We use CIP data to construct three variables: total number of instructional programmes (a proxy of educational complexity), shares of business-oriented programmes and technical-oriented programmes. We manually classify programmes into business-and technical-orientated to capture adjustments in education, and thus to match changes in the demand of the two main components of non-routine tasks: interactive and analytical.
Results are summarized in Table  12  where, for the sake of brevity, we focus on the comparison between Abstract occupations and all other occupations. The main finding is that increasing task variety is positively associated with employment growth within the group of abstract jobs, but not all for the other occupations. This is suggestive of a different margin of task reorientation for already complex occupations. Rather than merely requiring a more intensive use of non-routine tasks, such occupations adapt by increasing task variety  (Acemoglu and Restrepo, 2017) , multi-tasking  (Goerlich and Snower, 2013)  and cognitive-social task complementarities  (Deming, 2017). Obviously, the insights given by the present study on the different margins of task reorientation require further validation in the context of a homogeneous dataset, such as O*NET when a sufficient timespan will be available.
Francesco Vona wishes to acknowledge fruitful discussions with his former Ph. D. advisor,  Prof. Mario Amendola , from whom (several years ago now) he gained the much-needed insights for the inception of this project. Financial support acknowledgements: the  French government , through the  UCA-JEDI Investments  in the  Future  project managed by the  National Research Agency  [ ANR-15-IDEX-01 ] (FV);  CSIC and Ministerio de Economía y Empresa de España  [ ECO2017-86976-R] (DC).
Four electronic databases (PubMed, Web of Science, Cochrane, and Scopus) were searched, using the following keywords and combination of them: Postural control, Postural stability, Balance, Upright stance, Auditory cue, Acoustic stimulation, Vibratory stimulation, Acoustic Cue, Vibratory Cue, Vibrotactile stimulation, Healthy, Vibration, Postural Response. The strings used for the PubMed database have been reported here: [(postural control) OR (postural stability) OR (postural response) OR (balance) AND (auditory cue) OR (acoustic stimulation) OR (acoustic cue) AND (vibratory cue) OR (vibratory stimulation) OR (vibrotactile stimulation) OR (vibration) AND (healthy)],[(postural control) OR (postural stability) OR (postural response) OR (balance) AND (auditory cue) OR (acoustic stimulation) OR (acoustic cue) AND (healthy)], [(postural control) OR (postural stability) OR (postural response) OR (balance) AND (vibratory cue) OR (vibratory stimulation) OR (vibrotactile stimulation) OR (vibration) AND (healthy)].
Added value of this study The present study represents the most comprehensive comparative assessment of the magnitude and temporal trends of socioeconomic inequalities in cancer in Europe. Cancer-specific mortality data by socioeconomic status, as measured by educational level, were collected and harmonized across 18 countries in Europe and for multiple points in time over the period 1990-2015. We have assessed absolute and relative educational inequalities in cancer mortality, trends by educationlevel, sex, country, and cancer-type; and the share of cancer mortality linked with less-than-higher educational levels. Implications of all the available evidence Socioeconomic inequalities in mortality exist for most forms of cancer everywhere in Europe, with higher mortality rates for individuals at the lower ends of the social hierarchy.
Methods We collected and harmonized mortality data by cause of death, over extended periods of time, for 18 European countries spanning across regions of the continent [North: Norway, Sweden, Finland, Denmark, England/ Wales; West/South: Belgium, France, Switzerland, Austria, Italy (Torino department), Spain; Baltic/Central/East: Estonia, Lithuania, Poland, Czech Republic, Hungary, and Slovenia]. Most data stem from a longitudinal mortality follow-up at the individual level after a census. Deaths and person-years were classified according to 5-year age groups and follow up periods. For a few countries (Czech Republic, Hungary, Poland, Spain), separate, unlinked data sources were used for deaths and population-at-risk by age, sex and education. Most countries provide data about the entire national population, except England/Wales and France, for which large representative samples were used, and Italy and Spain, for which selected regions were included (Turin province and Barcelona, respectively).
To summarize EAPC in cancer mortality across countries, a random-effects model was used, with country-level random effects and with estimates weighted according to the population-size of the country. The share of total cancer mortality linked with less-than-higher educational levels, i. e. , lower-and intermediate-, was estimated by assuming a scenario based on the concept of plausible minimal risk where these groups have the same mortality rates as the higher-educated group, within each age-category. Confidence intervals (CIs) for the above describes statistics were computed using bootstrapping and reported as indicated. Throughout the rest of the text, the term "inequalities" is used to signify "educational inequalities in cancer mortality". Analyses were carried out in R and the code can be made available upon request.
Acknowledgements The permission of the  Office for National Statistics (ONS)  to use the Longitudinal Study is gratefully acknowledged, as is the help provided by staff of the  Centre for Longitudinal Study Information & User Support (CeLSIUS). CeLSIUS is supported by the  ESRC Census of Population Programme  (award reference  ES/K000365/1 ). The authors alone are responsible for the interpretation of the data. This work contains statistical data from  ONS , which is Crown Copyright. The use of the ONS statistical data in this work does not imply the endorsement of the ONS in relation to the interpretation or analysis of the statistical data. This work uses research datasets, which might not exactly reproduce National Statistics aggregates. Aggregated data on deaths and person years by age group, sex and level of education for Hungary between 1999 and 2012 were obtained and harmonized by  Erasmus MC  as part of the  DEMETRIQ  project and  LIFEPATH  project.
Funding This study was done as part of the  LIFEPATH  project, which has received financial support from the  European Commission (Horizon 2020  grant number  633666 ), and the  DEMETRIQ  project, which received support from the  European Commission  (grant numbers  FP7-CP-FP  and  278511 ). SV and WN were supported by the  French Institut National du Cancer (INCa)  (Grant number  2018-116 ). PM was supported by the  Academy of Finland  (# 308247 , #  345219 ) and the  European Research Council  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement No  101019329 ). The work by  Mall Leinsalu  was supported by the  Estonian Research Council  (grant  PRG722 ).
Objective To undertake a synthesis of evidence-based research for seven innovative models of care to inform the development of new hospitals. Design Umbrella review. Setting Interventions delivered inside and outside of acute care settings. Participants Children and adults with one or more identified acute or chronic health conditions. Data sources PsycINFO, Ovid MEDLINE and CINAHL. Primary and secondary outcome measures Clinical indicators and mortality, healthcare utilisation, quality of life, self-management and self-care and patient knowledge. Results A total of 66 reviews were included, synthesising evidence from 1272 primary studies across the 7 models of care. Virtual care was the most common model studied, addressed by 47 (73%) of the reviews. Common outcomes evaluated across reviews were clinical indicators and mortality, healthcare utilisation, self-care and selfmanagement, patient knowledge, quality of life and costeffectiveness.
Data sources and search strategy The search was designed in consultation with a research librarian and conducted across three databases: PsycINFO, Ovid MEDLINE and CINAHL. Online supplemental table 1 presents the search string for CINAHL used in the academic search. The screening of the reviews was undertaken in two steps: title/abstract screen and full-text screen. At both stages, all articles were independently screened by blinded pairs of reviewers on Rayyan (https://www. rayyan. ai/), a web-based collaborative tool. A total of 16 reviewers were paired and each pair allocated 926 articles at the title/abstract stage (7412/8 pairs). One pair was assisted by a third reviewer as one reviewer was unable to complete the screening. Three reviewers independently conducted an interrater reliability assessment on 5% of the articles during full-text screening (κ=1, 95% CI 0.
This systematic literature review aimed to provide an overview of the characteristics and methods used in studies applying the disability-adjusted life years (DALY) concept for infectious diseases within European Union (EU)/European Economic Area (EEA)/European Free Trade Association (EFTA) countries and the United Kingdom. Electronic databases and grey literature were searched for articles reporting the assessment of DALY and its components. We considered studies in which researchers performed DALY calculations using primary epidemiological data input sources. We screened 3053 studies of which 2948 were excluded and 105 studies met our inclusion criteria. Of these studies, 22 were multi-country and 83 were single-country studies, of which 46 were from the Netherlands. Food-and water-borne diseases were the most frequently studied infectious diseases.
Data sources and search strategy With the assistance of a specialist librarian from the Erasmus MC, we searched for burden of disease studies using five electronic bibliographic databases, platforms and search engines in week 22, 2022. We also performed a manual search for grey literature via public health websites from all the EU/EEA/EFTA countries to retrieve governmental documents. To foster comprehensiveness in grey literature searches, we asked the burden-eu members to provide eligible grey literature from their countries. We also searched for grey literature via other websites which are included in the Supplementary Material. Finally, we checked the reference lists of eligible systematic reviews identified in the above searches. Details of the search strategy and list of the targeted EU/EEA/ EFTA countries and the United Kingdom and public health websites can be found in the Supplementary Material.
Inclusion and exclusion criteria We restricted our analysis to studies assessing the burden due to infectious diseases in terms of YLL, YLD and/or DALY, utilizing their own national or sub-national data based on primary data input sources. We included multi-national studies, as long as they fulfilled the criteria above. Since the DALY concept was introduced in the 'World Development Report 1993: Investing in Health'  [34] , we considered only disease burden studies published after January 1990. We considered studies in which the infection was defined as an illness due to a pathogen arising through transmission from an infected individual (i. e. human-human transmission), an infected animal (i. e. direct animal contact) or from other pathways (i. e. food, travel, etc. )  [35]. We excluded studies that performed secondary or systematic analyses based on the GBD estimates.
Data screening and extraction Two researchers (PC and VG) listed all the records obtained from the grey literature searches, reference checks and the burden-eu members, on an Excel spreadsheet. PC manually imported all these records to the EndNote X9 library provided by the Erasmus MC. After removing duplicates, PC and VG independently reviewed the resulting article titles (step 1) and abstracts (step 2) against the inclusion and exclusion criteria mentioned above. After this exclusion, PC and VG subsequently identified and screened potentially relevant burden of infectious disease studies upon full-text (step 3). When the two researchers disagreed on whether to include or exclude an article, they discussed their doubts with JH and LSJ. Two researchers (PC and LSJ) critically appraised burden of infectious disease studies written in English using an adapted data extraction form developed for an earlier systematic review  [24].
For studies written in languages that could not be read by any of the reviewers, PC organised online meetings with native speakers to perform the data extraction. We extracted information relating to the following items: study characteristics (i. e. author, title, aim, year of publication, infectious disease category studied, reference country/region included) as well as methodological approaches used to calculate YLL (i. e. choice of life table) and/or YLD (i. e. incidencevs. prevalence-based approach and pathogenvs. outcome-based approach, disability weights and social weighting functions). Definitions of the extracted items can be found in the Supplementary Material. PC and LSJ compared, assessed and discussed the final version of the completed data extraction form. Discussions with JH resolved any possible doubts.
Literature search We identified 3376 records through database, grey literature and citation searching. After removing duplicates (n = 323), we screened titles and abstracts from 3053 records. We assessed 459 full-text articles for eligibility having excluded 354 studies because they did not meet the inclusion criteria: 319 were excluded because other data input sources than those we specified were used; 28 were excluded because they reported results from the same study; and seven because other health metrics than YLL, YLD and/or DALY were used to express the burden of disease. Finally, we extracted information from 105 burden of infectious disease studies (Fig. 1 ).
Strengths and limitations Although we have reviewed a variety of electronic databases, platforms and search engines; grey literature searches may have been limited. Nevertheless, independent burden of infectious disease studies in EU/EEA/EFTA countries and the United Kingdom have been identified and categorised by study characteristics resulting in an overview of DALY-specific methodological design choices that were used in burden of infectious disease studies over the period from 2000 to 2022. Moreover, in contrast to what is systematically performed in literature review, we did not perform a risk of bias assessment of the included burden of infectious disease studies, since the existing assessment tools were not suitable for evaluating the quality of burden of disease studies.
Acknowledgements. The authors wish to thank  Maarten Engel  from the  Erasmus MC Medical Library  for developing and updating the search strategies. The authors would also like to acknowledge the networking support from  COST Action CA18218 (European Burden of Disease Network ; www. burden-eu. net),supported by  COST (European Cooperation in Science and Technology ; www. cost. eu). Author contributions. PC, JH and SMP developed the study design; PC, VG and LSJ performed the screening and/or data extraction; PC, JH, SMP, LSJ, VG, BD, SM, GMAW, DP and JVS analysed and interpreted the data; PC and JH drafted the manuscript. All authors read, critically revised the manuscript and approved the final manuscript. Financial support. This research received no specific grant from any funding agency in the public, commercial or not-for-profit sectors.
1-3) STATISTICAL ANALYSIS Values are reported as means ± standard deviations. The statistical analysis was performed using Statistica 6. 1. Distributions normalities were tested by Shapiro-Wilk test. Descriptive statistics were calculated to demonstrate the groups' baseline sociodemographic characteristics. A two-way analysis of variance (ANOVA) was used to compare values of cognitive, psychological, behavioural, and physical functions between groups (dance group versus gymnastic group) at the beginning and at the end of the physical exercise program (pre versus post). Post hoc comparisons were made using Scheffe's test. Effect sizes were calculated using partial eta squared (η 2 p) and Cohen's d. A significance level was set at 5% for all statistical analyses.
Afterwards, the link between sleep disorders and weight gain was clarified and behavioral strategies were mentioned to improve sleep quality. Finally, negative body image has been dealt with through posts explaining the role of developmental factors such as pressures to reach occidental beauty standards usually conveyed by the media, past interpersonal experiences, morphological characteristics, cognitive distortions, checking and avoidance behaviors in the maintenance of body dissatisfaction. Throughout this axis, change was encouraged through self-observational and exposition exercises  (28)  to reach the objective of a better lifestyle. A dedicated platform was created for the program, allowing for the secure registration of participants protected via an individual password. Personal data and registration forms were collected directly through this secured online platform in accordance with the Swiss regulation (ISO 27001 certification).