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Record W2336476267 · doi:10.7202/1035435ar

Enjeux de santé des aînés francophones vivant en situation minoritaire : une analyse différenciée selon les sexes1

2016· article· fr· W2336476267 on OpenAlexaffvenueabout
Solange van Kemenade, Louise Bouchard, Christian Bergeron

Bibliographic record

VenueReflets Revue d’intervention sociale et communautaire · 2016
Typearticle
Languagefr
FieldHealth Professions
TopicInterpreting and Communication in Healthcare
Canadian institutionsUniversité de MontréalMontfort HospitalUniversité du Québec en Outaouais
Fundersnot available
KeywordsHumanitiesPolitical scienceSociologyArt

Abstract

fetched live from OpenAlex

La recherche en santé sur les Communautés de langue officielle vivant en situation minoritaire révèle des disparités. Le fait d’être minoritaire dans une société, conjointement avec d’autres déterminants, comme le statut socioéconomique, l’éducation en santé, et le fait de ne pas avoir un soutien social contribuent aux disparités de santé. Dans le contexte du vieillissement plus marqué des populations de langue officielle vivant en situation minoritaire, nous avons examiné la situation des aînés francophones. Pour cela, une analyse secondaire des données issues de l’Enquête sur la santé des collectivités canadiennes (ESCC) a été réalisée. Nous présentons dans cet article, une analyse différenciée de leur situation sociosanitaire selon le sexe et le genre en dressant le portrait des états de santé, des incapacités et des besoins sur le plan des soins de santé.

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)0.007
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.620
Threshold uncertainty score0.755

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0030.002
Scholarly communication0.0030.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.060
GPT teacher head0.433
Teacher spread0.373 · 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 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

Citations12
Published2016
Admission routes3
Has abstractyes

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