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Record W3133464095 · doi:10.1177/1757975920986128

Défis de l’action intersectorielle et pistes de réflexion pour renforcer la concertation dans les milieux ruraux du Québec en contexte de pandémie

2021· article· fr· W3133464095 on OpenAlexaffabout
J. Richard, Dave A. Bergeron, Lily Lessard, Isabelle Toupin, Nicole Ouellet, Emmanuelle Bédard

Bibliographic record

VenueGlobal Health Promotion · 2021
Typearticle
Languagefr
FieldSocial Sciences
TopicSocial Sciences and Governance
Canadian institutionsCentre intégré de santé et de services sociaux de Chaudière-AppalachesUniversité du Québec à Rimouski
Fundersnot available
KeywordsPolitical scienceHumanitiesPhilosophy

Abstract

fetched live from OpenAlex

La présente pandémie nécessite le recours aux mécanismes qui favorisent l’action intersectorielle entre les autorités et les partenaires de différents secteurs de la santé et de la société civile pour coordonner et adapter la réponse socio-sanitaire en fonction des particularités des milieux et de l’évolution de la pandémie. Ce commentaire propose de mettre en lumière quelques défis qui se posent actuellement dans la mise en œuvre d’actions intersectorielles dans les milieux ruraux du Québec. Des pistes de réflexion en faveur du renforcement des mécanismes de concertation nécessaires à la gestion de la pandémie sont proposées.

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)0.020
metaresearch head score (Gemma)0.020
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.391
Threshold uncertainty score0.786

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0200.020
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0120.039
Scholarly communication0.0140.005
Open science0.0020.007
Research integrity0.0040.006
Insufficient payload (model declined to judge)0.0080.000

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 head0.048
GPT teacher head0.399
Teacher spread0.351 · 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 designQualitative
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

Citations10
Published2021
Admission routes2
Has abstractyes

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