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Record W4255686558 · doi:10.32920/ryerson.14643693.v1

Within These Walls: Exploring the Mental Health Experiences of First Generation South Asian Women in Toronto Canada

2021· preprint· en· W4255686558 on OpenAlexaffabout
Aneesa Atta

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsMental healthMental health serviceMiddle Eastern Mental Health Issues & SyndromesPerspective (graphical)South asiaPsychologyNarrativeQualitative researchStressorService providerNursingMedicineService (business)Mental health lawSociologyPsychiatryBusinessSocial science

Abstract

fetched live from OpenAlex

This research paper explores the mental health experiences of first-generation South Asian women in Toronto, Canada. This research paper starts by providing a brief overview of mental health literature from a South Asian perspective. A qualitative narrative methodology is used to explore what mental health experiences are faced by first-generation South Asian women and their experience of accessing support services in Toronto. Three individual interviews were conducted. Participants were provided with an opportunity to share their experiences of what mental health and recovery mean to them, the mental health stressors they face, the different barriers they encounter when accessing mental health services, and coping strategies employed. This research contributes to a broader understanding of mental health within the South Asian communities and how mental health service providers can work towards a more inclusive and culturally responsive practice when supporting South Asian women.

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 score0.052
Threshold uncertainty score0.375

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0240.008
Scholarly communication0.0040.002
Open science0.0020.006
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0050.000

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 head0.061
GPT teacher head0.322
Teacher spread0.261 · 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

Citations1
Published2021
Admission routes2
Has abstractyes

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