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Record W2191053811 · doi:10.1017/s0714980815000379

Mental Health and Service Issues Faced by Older Immigrants in Canada: A Scoping Review

2015· review· fr· W2191053811 on OpenAlexaffabout
Sepali Guruge, Mary Susan Thomson, Sadaf Grace Seifi

Bibliographic record

VenueCanadian Journal on Aging / La Revue canadienne du vieillissement · 2015
Typereview
Languagefr
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsHumanitiesPolitical scienceImmigrationPhilosophy

Abstract

fetched live from OpenAlex

RÉSUMÉ Une population vieillissante et la croissance de la population sur la base de l'immigration nécessitent que la recherche, la pratique et la politique doivent se concentrer sur la santé mentale des immigrants âgés, surtout parce que leur santé mentale semble se détériorer au fil du temps. Cette revue se concentre sur: Qu'est-ce que l'on sait sur les déterminants sociaux de la santé mentale chez les immigrants âgés, et quels sont les obstacles à l'accès aux services de santé mentale confrontés par les immigrants âgés? Les résultats révèlent que (1) les déterminants sociaux décisifs de la santé mentale sont la culture, le sexe et les services de santé; (2) que les immigrants plus âgés utilisent les services de santé mentale de moins que leurs homologues nés au Canada à cause des obstacles tels que, par exemple, les croyances et les valeurs culturelles, un manque de services culturellement et linguistiquement appropriées, des difficultés financières, et l'âgisme; et (3) quelles que soient les sous-catégories dans cette population, les immigrants âgés éprouvent des inégalités en matière de la santé mentale. La preuve des recherches disponibles indique que de combler les lacunes des service de santé mentale devrait devenir une priorité pour la politique et la pratique du système de soins de santé au Canada.

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)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.355
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.030
GPT teacher head0.322
Teacher spread0.292 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations62
Published2015
Admission routes2
Has abstractyes

Explore more

Same venueCanadian Journal on Aging / La Revue canadienne du vieillissementSame topicMigration, Health and TraumaFrench-language works237,207