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Record W4412366771 · doi:10.1522/rhe.v9i3.1777

Diversité et santé mentale en milieu universitaire : perspectives des informatrices et des informateurs clés sur l’accès aux services

2025· article· fr· W4412366771 on OpenAlexaffvenue
Christiane Bergeron‐Leclerc, Fatoumata Diadiou, Jacques Cherblanc, Isabel Ouellet-Gagné, Maryève Savard, Camille Mercure, Eve Boily, Mateo Isaac Laguna Muñoz, Dominique Tremblay, Danielle Maltais

Bibliographic record

VenueRevue hybride de l éducation · 2025
Typearticle
Languagefr
FieldSocial Sciences
TopicEducation, sociology, and vocational training
Canadian institutionsUniversité du Québec à Chicoutimi
Fundersnot available
KeywordsPolitical science

Abstract

fetched live from OpenAlex

La diversité en milieu universitaire est une richesse qui s'accompagne de défis significatifs, notamment en matière de santé mentale. Cette étude qualitative vise à explorer les perspectives d’informatrice et d’informateurs clés sur l'accès aux services de santé mentale dans un contexte universitaire. Pour y parvenir, 27 entrevues semi-dirigées ont été menées auprès de personnes cadres, employées et étudiantes. Prenant appui sur l’approche bioécologique, l’analyse thématique a permis d’identifier les facilitateurs et les obstacles d’accès aux services de santé mentale dans les milieux universitaires. Les principaux facilitateurs comprennent la diversité de l’offre de services, la proximité des services d’intervention, les qualités d’être des personnes intervenantes et la promotion des services disponibles. En revanche, les principaux obstacles incluent les barrières linguistiques, l’isolement, l’étalement géographique, la méconnaissance et la méfiance envers les services ainsi que la taille des institutions. Cette étude offre des pistes pour améliorer l'accès aux services de santé mentale en milieu universitaire et leur efficacité, en soulignant l'importance de stratégies inclusives et adaptées aux besoins diversifiés des personnes étudiantes.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.113
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.003
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.108
GPT teacher head0.415
Teacher spread0.307 · 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 teacher head, not a consensus.

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

Citations0
Published2025
Admission routes2
Has abstractyes

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