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Record W4399737079 · doi:10.24095/hpcdp.44.6.06f

Mouvement étudiant pour la prescription sociale au Canada

2024· article· fr· W4399737079 on OpenAlexaffvenueabout
Caitlin Muhl, Le‐Tien Bhaskar, Michelle Ruhigisha, Ellen McGarity-Shipley

Bibliographic record

VenuePromotion de la santé et prévention des maladies chroniques au Canada · 2024
Typearticle
Languagefr
FieldArts and Humanities
TopicArt Therapy and Mental Health
Canadian institutionsBrock UniversityMcMaster UniversityQueen's University
Fundersnot available
KeywordsHumanitiesPolitical scienceMedical prescriptionMedicineArtNursing

Abstract

fetched live from OpenAlex

Introduction La prescription sociale est « un moyen par lequel des professionnels de confiance en milieu clinique ou communautaire déterminent qu’une personne a des besoins sociaux liés à la santé qui ne sont pas d’ordre médical, et par lequel ils adressent cette personne à des services et à un soutien non cliniques au sein de la communauté, par l’élaboration conjointe d’une « prescription sociale », c’est-à-dire une prescription non médicale pour améliorer la santé et le bien-être de la personne et renforcer ses liens avec la communauté ». À l’échelle mondiale, la prescription sociale suscite un intérêt croissant en tant qu’approche holistique de la santé et du bien-être, et près de 30 pays sont impliqués dans le mouvement pour la prescription sociale. Au Canada, la recherche, les politiques et les pratiques en matière de prescription sociale progressent à grands pas avec le soutien de l’Institut canadien de prescription sociale.

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)0.003
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.097
Threshold uncertainty score0.703

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.005
Science and technology studies0.0090.002
Scholarly communication0.0040.001
Open science0.0020.002
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0140.001

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 head0.017
GPT teacher head0.286
Teacher spread0.269 · 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 designNot applicable
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

Citations1
Published2024
Admission routes3
Has abstractyes

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