Bienfaits psychologiques de l’activité physique pour la santé mentale optimale
Bibliographic record
Abstract
Mental health is a worldwide public health concern, as can be seen from the WHO's comprehensive mental health action plan 2013-2020 which was adopted by the 66th World Health Assembly. According to the Mental health commission of Canada (2012), one in five Canadians will personally experience a mental illness in their lifetime, and the WHO shows that mental illness represents the second most prevalent risk of morbidity after heart disease. Physical activity certainly provides an answer to this problem. Physical activity has been shown to improve physical health but it is also one of the most natural and accessible means to improve mental health. The aim of the present article is to propose a biopsychosocial model on the basis of a literature review on the psychological benefits of physical activity. In view of the findings we assume that physical activity increases mental well-being and optimal mental health as opposed to poor mental health. Hence, physical activity provides a state of well-being that enables individuals to realize their own potential, and that helps to cope with the normal stresses of life or adversity. The model certainly opens the way for research and new hypothesis, but it also aims at the promotion of the benefits of physical activity on psychological well-being for optimal mental health.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.019 | 0.002 |
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".