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Record W2896944942 · doi:10.7202/1051101ar

LES ENJEUX INTERSECTIONNELS DE LA DEMANDE DE SERVICES DE SANTÉ MENTALE AU CANADA

2018· article· fr· W2896944942 on OpenAlexvenueaboutno aff
Florina Gaborean, Lilian Negura, Nicolas Moreau

Bibliographic record

VenueCanadian social work review · 2018
Typearticle
Languagefr
FieldSocial Sciences
TopicCanadian Identity and History
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesPolitical scienceArt

Abstract

fetched live from OpenAlex

Dans cet article, nous présentons, selon le cadre théorique féministe intersectionnel, une étude visant à mieux comprendre les dynamiques identitaires qui influencent la demande de services de santé mentale des jeunes femmes francophones vivant en situation minoritaire ainsi que les effets croisés de celles-ci sur les facteurs déclencheurs de la dépression, la perception des symptômes et le parcours thérapeutique. Notre analyse révèle que l’imbrication de plusieurs catégories d’inégalité complexifie l’accès aux services de santé mentale des jeunes femmes dépressives en situation minoritaire tout en favorisant l’émergence de stratégies de lutte et de résistance. Le croisement des catégories identitaires de genre, d’âge et de langue parlée produit des effets compensatoires sur la demande de services de santé mentale. Alors que l’image sociale positive de la jeunesse les fait hésiter dans la demande de services, celle liée à l’identité féminine légitime, la dépression et les faiblesses qui pourraient en découler. Les femmes mettent en oeuvre des stratégies pour pallier les inégalités d’accès aux services de santé mentale en français et trouver les services qu’elles considèrent appropriés pour leur problème spécifique. Au lieu de favoriser la discrimination ou la marginalisation, cette imbrication d’appartenances identitaires multiples permet le renforcement du pouvoir d’agir des jeunes femmes francophones.

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)0.002
metaresearch head score (Gemma)0.010
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: Empirical
Teacher disagreement score0.075
Threshold uncertainty score0.543

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.004
Science and technology studies0.0090.004
Scholarly communication0.0060.002
Open science0.0010.005
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0100.000

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 head0.016
GPT teacher head0.282
Teacher spread0.266 · 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
Published2018
Admission routes2
Has abstractyes

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