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Record W4416687725 · doi:10.34719/eevo8164

Physical activity and gaming activity among adolescents with disabilities

2025· article· W4416687725 on OpenAlexaboutno aff
Kwok Ng

Bibliographic record

Venuenot available
Typearticle
Language
FieldPsychology
TopicChildren's Physical and Motor Development
Canadian institutionsnot available
Fundersnot available
KeywordsPhysical activityPsychological interventionActivities of daily livingQuarter (Canadian coin)Physical activity level

Abstract

fetched live from OpenAlex

Introduction Physical activity (PA) is protective for health, particularly for adolescents with disabilities. Less evidence is available on digital gaming activity (GA) on health, although a popular activity among adolescents. The aim of the study was to compare levels of PA and GA among Finnish adolescents, disaggregated by disabilities. Methods The Finnish school-age PA study is a nationally representative study of 11y-, 13y-, 15y-, and 16y-20y old adolescents. The self-report version of the UNICEF/Washington group questions on disabilities were used. Differences between with and without disabilities were analysed by Chi-square tests of independence after stratification by age and gender. Results Almost a quarter of respondents (23%) had disabilities. Males with disabilities reported statistically significantly less daily PA (25%) than the ones without disabilities (33%). The corresponding percentages in females were 16 % and 22%. Low PA (0-2 days/week) were higher among the individuals with disabilities compared to those without (males 20 % vs, 11%, females 20% vs. 9%, p<.001). Differences in daily GA amongst males with (38%) and without (33%) disabilities were not statistically significant. Daily GA among females with disabilities (15%) were significantly (p <.001) higher than without disabilities (8%). Discussion More must be done to promote PA among adolescents with disabilities. Female adolescents with disabilities, especially those with social-behavioural difficulties, had the lowest levels of PA, yet had higher than expected rates of daily GA. The underlying reasons for these patterns warrant further investigation, with potential targeted interventions that combine both PA and GA.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.021
Threshold uncertainty score0.042

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.012
GPT teacher head0.281
Teacher spread0.268 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2025
Admission routes1
Has abstractyes

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