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Record W2726492897 · doi:10.7202/1040248ar

Bienfaits psychologiques de l’activité physique pour la santé mentale optimale

2017· article· fr· W2726492897 on OpenAlexvenueaboutno aff
Emmanuel Poirel

Bibliographic record

VenueSanté mentale au Québec · 2017
Typearticle
Languagefr
FieldMedicine
TopicCardiac Health and Mental Health
Canadian institutionsnot available
Fundersnot available
KeywordsBiopsychosocial modelMental healthPsychologyAction (physics)Physical activityHealth promotionAction planPromotion (chess)Public healthMental illnessPsychiatryGerontologyMedicinePolitical scienceNursingPhysical therapy

Abstract

fetched live from OpenAlex

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.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.748
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.026
GPT teacher head0.385
Teacher spread0.358 · 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; both teacher heads agree on what is shown here.

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

Citations28
Published2017
Admission routes2
Has abstractyes

Explore more

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