Facteurs dont il faut tenir compte dans la création d’un guide d’activité physique pour les Canadiens qui ont un handicap physiqueCet article est tiré d’un supplément intitulé<i>Advancing physical activity measurement and guidelines in Canada: a scientific review and evidence-based foundation for the future of Canadian physical activity guidelines</i>(Favoriser les lignes directrices et la mesure de l’activité physique au Canada: examen scientifique et justification selon les données probantes pour l’avenir des lignes directrices de l’activité physique canadienne) publié par<i>Physiologie appliquée, nutrition et métabolisme</i>et la<i>Revue canadienne de santé publique</i>. On peut aussi mentionner Appl. Physiol. Nutr. Metab. 32 (Suppl. 2F) ou Can. J. Public Health 98 (Suppl. 2).
Bibliographic record
Abstract
This paper provides a brief overview of the epidemiological data regarding Canadians with physical disabilities, with a particular emphasis on health status. A literature review is then presented, focusing on activity levels and the physiological and health-related quality of life benefits of activity for people with four different physical disabilities (arthritis, fibromyalgia, spinal cord injury, and multiple sclerosis). The unique physical activity barriers faced by people with physical disabilities are discussed. The paper concludes with recommendations for the development of a physical activity guide for Canadians with physical disabilities.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.026 | 0.010 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".