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Record W3185633712 · doi:10.1177/14034948211031392

The epidemiology of muscle-strengthening activity among adolescents from 28 European countries

2021· article· en· W3185633712 on OpenAlexaff
Jason A. Bennie, Guy Faulkner, Jordan Smith

Bibliographic record

VenueScandinavian Journal of Public Health · 2021
Typearticle
Languageen
FieldMedicine
TopicSports injuries and prevention
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsOverweightPoisson regressionMedicineConfidence intervalDemographyEpidemiologyPublic healthPopulationGuidelinePsychological interventionGerontologyEnvironmental healthObesityInternal medicine

Abstract

fetched live from OpenAlex

AIMS: This study aimed to describe the prevalence and socio-demographic and lifestyle-related correlates of muscle-strengthening activity (MSA; strength/resistance training, sit-ups/push-ups, etc.) among a large sample of European adolescents. METHODS: Data were drawn from the European Health Interview Survey Wave 2 (2013-2014), including 8818 adolescents (15-17 years) from 28 European countries. Self-reported MSA was assessed using a previously validated survey item. Population-weighted prevalence ratios were calculated for (a) 'none' (0 days/week), (b) 'insufficient MSA' (1-2 days/week) or (c) 'sufficient MSA' (⩾3 days/week). Generalised linear models using Poisson regression with robust error variance were used to calculate the prevalence ratios for adolescents reporting sufficient MSA by socio-demographic/lifestyle characteristics and by European region. RESULTS: Overall, 19.4% (95% confidence interval (CI) 18.3-20.7) reported sufficient (⩾3 days/week) MSA and 57.9% (95% CI 56.4-59.6) reported none. Females, adolescents from Southern and Eastern European regions, those not meeting the aerobic guideline and adolescents classified as overweight were significantly associated with a lower likelihood of reporting sufficient MSA, independent of other characteristics.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.159
Threshold uncertainty score0.306

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.059
GPT teacher head0.342
Teacher spread0.283 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations25
Published2021
Admission routes1
Has abstractyes

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