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Record W4399737277 · doi:10.24095/hpcdp.44.6.07f

Prescription sociale à l’intention des personnes noires : l’importance d’une approche afrocentrique

2024· article· fr· W4399737277 on OpenAlexaffvenue
Sofia Ramirez, Natasha Beaudin, Jennifer Rayner, Neil Price, D. Townsend

Bibliographic record

VenuePromotion de la santé et prévention des maladies chroniques au Canada · 2024
Typearticle
Languagefr
FieldDecision Sciences
TopicEvaluation and Performance Assessment
Canadian institutionsLogicalOutcomes
Fundersnot available
KeywordsPolitical scienceHumanitiesMedical prescriptionArtMedicine

Abstract

fetched live from OpenAlex

Résumé Le projet de prescription sociale à l’intention des Noirs est une initiative originale de l’Alliance pour des communautés en santé qui associe les principes afrocentriques à la prescription sociale. Au-delà des modèles conventionnels de prescription sociale, ce projet répond à des besoins particuliers en matière de santé au sein des communautés noires. Il est ancré dans la Stratégie de promotion de la santé des Noirs de l’Alliance, milite pour la santé des personnes noires et est guidé par les principes afrocentriques. Le cadre d’évaluation accorde la priorité à la voix des clients, assure la sécurité culturelle et, grâce au temps consacré à l’instauration d’un climat de confiance, souligne l’importance d’une approche inclusive. Le projet de prescription sociale à l’intention des Noirs a le potentiel de favoriser la confiance et la mobilisation de la communauté et d’améliorer les résultats en matière de santé dans la communauté noire.

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)0.097
metaresearch head score (Gemma)0.130
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 score0.743
Threshold uncertainty score0.515

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0970.130
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.003
Science and technology studies0.0040.007
Scholarly communication0.0090.004
Open science0.0030.005
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0080.001

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 head0.047
GPT teacher head0.380
Teacher spread0.333 · 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 routes2
Has abstractyes

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