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Record W4313827853 · doi:10.3917/rsi.150.0079

Les expériences de patients francophones qui se présentent fréquemment à l’urgence pour des raisons de santé mentale

2022· article· fr· W4313827853 on OpenAlexaffabout
Amanda Vandyk, Sophie Lightfoot, Kristine Levesque, Marie‐Cécile Domecq, Jean Daniel Jacob

Bibliographic record

VenueRecherche en soins infirmiers · 2022
Typearticle
Languagefr
FieldHealth Professions
TopicInterpreting and Communication in Healthcare
Canadian institutionsMontfort HospitalUniversity of Ottawa
Fundersnot available
KeywordsHumanitiesPolitical scienceSociologyArt

Abstract

fetched live from OpenAlex

Introduction : la langue et la communication sont essentielles à la sécurité des soins et à la gestion des personnes aux prises avec un trouble de santé mentale, en particulier lorsque ces personnes sont des minorités linguistiques. Objectifs/méthode : explorer ces réalités au sein d’une population en situation minoritaire linguistique en complément d’une revue de la littérature et des entrevues qualitatives. Les entrevues ont été menées à Ottawa, au Canada, auprès de patients francophones. Les études incluses dans la revue représentaient la littérature internationale sur les minorités linguistiques en général. Résultats : dans l’ensemble, les expériences décrites dans les articles publiés étaient semblables aux expériences vécues des participants, ce qui suggère que des obstacles aux soins existent, même dans les contextes ayant pour mandat de fournir des services dans les deux langues officielles. Discussion/conclusion : il y a de nombreux obstacles à la prestation de services de soins de santé mentale, et ce, quelle que soit la langue dominante. Toutefois, nous avons identifié comme distinct le sentiment intériorisé de responsabilité ressenti par les patients en situation minoritaire qui se sentent obligés de compenser ou de combler les lacunes linguistiques des prestataires.

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)0.004
metaresearch head score (Gemma)0.009
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.191
Threshold uncertainty score0.381

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0060.004
Scholarly communication0.0030.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.001

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 head0.282
GPT teacher head0.514
Teacher spread0.233 · 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

Citations3
Published2022
Admission routes2
Has abstractyes

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