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Record W2106718780 · doi:10.3138/cpp.2014-074

Qualité de l’emploi et santé mentale des travailleurs au Québec : une comparaison entre les immigrants et les natifs

2015· article· fr· W2106718780 on OpenAlexaffvenueabout
Maude Boulet, Brahim Boudarbat

Bibliographic record

VenueCanadian Public Policy · 2015
Typearticle
Languagefr
FieldHealth Professions
TopicEmployment and Welfare Studies
Canadian institutionsUniversité de MontréalMcGill University
Fundersnot available
KeywordsPolitical scienceHumanitiesPhilosophy

Abstract

fetched live from OpenAlex

Dans cet article, nous analysons le lien d’association entre la qualité de l’emploi et la santé mentale des travailleurs nés au Canada ou à l’étranger. Il s’agit de vérifier si le fait d’occuper un emploi de qualité préserve la santé mentale des travailleurs, et celle immigrants en particulier. En effet, la littérature souligne que l’intégration des immigrants sur le marché du travail au Canada est difficile et que le chômage est l’un des principaux stresseurs de la santé mentale des nouveaux arrivants. Nos résultats – obtenus à partir des données de l’Enquête québécoise sur des conditions de travail, d’emploi et de santé et de sécurité du travail(EQCOTESST) – révèlent que le fait d’occuper un emploi de qualité réduit davantage la détresse psychologique chez les immigrants que chez les natifs. Cela suggère que la qualité de l’emploi aurait un effet protecteur plus fort sur la santé mentale des immigrants que sur celle des natifs. L’investissement dans l’intégration des immigrants serait de ce fait plus que rentable pour la société d’accueil.

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)0.001
metaresearch head score (Gemma)0.003
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.025
Threshold uncertainty score0.116

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0030.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.142
GPT teacher head0.428
Teacher spread0.286 · 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

Citations10
Published2015
Admission routes3
Has abstractyes

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Same venueCanadian Public PolicySame topicEmployment and Welfare StudiesFrench-language works237,207