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Record W7133635988 · doi:10.1080/13674676.2025.2500470

Barriers and facilitators towards recovery among Afro-Caribbean mental health service users in Canada

2025· article· en· W7133635988 on OpenAlexafffundabout
Justin Muthaih, G. Eric Jarvis, Rob Whitley

Bibliographic record

VenueMental Health Religion & Culture · 2025
Typearticle
Languageen
FieldHealth Professions
TopicMental Health and Patient Involvement
Canadian institutionsJewish General HospitalMcGill UniversityDouglas Mental Health University Institute
FundersSocial Sciences and Humanities Research CouncilSocial Sciences and Humanities Research Council of Canada
KeywordsMental healthMental health serviceService (business)Mental illnessPsychological interventionHealth careQuality (philosophy)

Abstract

fetched live from OpenAlex

We examined experiences among Afro-Caribbean mental health service users in Canada, exploring (i) barriers and facilitators towards recovery; (ii) experiences within the official health care system; and (iii) utilisation of alternative treatments and remedies. We conducted 18 in-depth interviews with Afro-Caribbean service users, and subsequent thematic analysis revealed three overlapping themes. First, participants pointed to their Christian faith as a source of comfort. Second, participants often reported that stigma in their family and community was a barrier to recovery. Third, many reported problematic issues within clinical services, including avoiding discussion of religion and spirituality, and a perceived overemphasis on medication. This study reveals that Afro-Caribbean mental health service users in Canada are experiencing many of the same issues that were identified in studies occurring decades ago, suggesting the need for concerted action. This could include cultural and religious competence training for Canadian clinicians and anti-stigma campaigns targeting minority communities.

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)0.005
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 score0.034
Threshold uncertainty score0.186

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0140.003
Scholarly communication0.0030.001
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.039
GPT teacher head0.353
Teacher spread0.314 · 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

Citations0
Published2025
Admission routes3
Has abstractyes

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