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Record W4391774316 · doi:10.32920/25212980

Black Muslim Immigrant Seniors Accessing Mental Health Services in the Greater Toronto Area

2024· preprint· en· W4391774316 on OpenAlexaffabout
Ilham Jilani

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsPhotovoiceMental healthDistrustImmigrationNarrativeLived experienceGender studiesQualitative researchNarrative inquiryRacismSociologyMedicineNursingPsychologyPolitical sciencePsychiatry

Abstract

fetched live from OpenAlex

This narrative approach to qualitative research explores the experiences of Black Muslim immigrant seniors in relation to accessing mental health services in the Greater Toronto Area. This study involved interviewing three Black African Muslim immigrant seniors living in Toronto. The participants shared their insight on the lack of culturally and linguistically diverse services as well as the systemic barriers they face as racialized seniors living in a Western society. Through the exploration of storytelling, participants identified pre and post migration traumas, shared coping strategies, expressed their fears and extreme distrust of white medical professionals and offered recommendations to better serve this ever-growing population. Critical gerontology and anti-black racism theories were used to help frame and guide discussions as well as further comprehend the participants’ lived experiences. This study also implements photovoice activities to gain a deeper insight into the participants’ experiences of navigating mental health resources.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.585
Threshold uncertainty score0.824

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0110.005
Scholarly communication0.0020.001
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.032
GPT teacher head0.361
Teacher spread0.329 · 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
Published2024
Admission routes2
Has abstractyes

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