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Record W2134909033 · doi:10.1080/01612840903033733

Primary Mental Health Care Information and Services for St. John's Visible Minority Immigrants: Gaps and Opportunities

2009· article· en· W2134909033 on OpenAlexaffabout
Sylvia Reitmanova, Diana L. Gustafson

Bibliographic record

VenueIssues in Mental Health Nursing · 2009
Typearticle
Languageen
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsSt. John’s Health Sciences CentreMemorial University of Newfoundland
Fundersnot available
KeywordsMental healthImmigrationStigma (botany)Language barrierService providerCultural competenceLiteracyHealth careMental health literacyCultural diversityNursingPublic relationsService (business)Mental illnessMedicineBusinessPsychologyPolitical sciencePsychiatryMarketing

Abstract

fetched live from OpenAlex

This article draws on an environmental scan and interviews with visible minority immigrants in a small urban Atlantic community to report on gaps and opportunities for improving access to information about primary mental health care services and barriers to utilization of these services. Information about services was limited and did not specifically address the complex health-related concerns of immigrants with diverse religious and cultural backgrounds. Accessing information about mental health care services was challenging for some visible minority immigrants because of physical and financial constraints and limited computer and language literacy. The major barriers to the utilization of primary mental health care services were lack of information, language and literacy issues, a mistrust of primary mental health care services, the stigma associated with mental illness, long wait times, lack of finances, and religious and cultural differences and insensitivity. A list of nine recommendations, which may be of interest to mental health decision-makers and service providers in small urban centers with limited ethno-cultural diversity, is provided.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.643
Threshold uncertainty score0.908

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
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 head0.022
GPT teacher head0.372
Teacher spread0.350 · 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.

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

Citations38
Published2009
Admission routes2
Has abstractyes

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