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Record W2091890670 · doi:10.1300/j013v40n04_07

Voices of South Asian Women: Immigration and Mental Health

2005· article· en· W2091890670 on OpenAlexafffundabout
Farah Ahmad, Angela Shik, Reena Vanza, Angela M. Cheung, Usha George, Donna E. Stewart

Bibliographic record

VenueWomen & Health · 2005
Typearticle
Languageen
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsUniversity of TorontoUniversity Health Network
FundersCanadian Institutes of Health Research
KeywordsMental healthStressorImmigrationCoping (psychology)Focus groupHealth carePsychologyQualitative researchSocial supportEthnic groupMedicineGerontologyClinical psychologySocial psychologyPsychiatryPolitical scienceSociology

Abstract

fetched live from OpenAlex

PURPOSE: This qualitative research aimed to elicit experiences and beliefs of recent South Asian immigrant women about their major health concerns after immigration. METHODS: Four focus groups were conducted with 24 Hindi-speaking women who had lived less than five years in Canada. The audiotaped data were transcribed, translated, and analyzed by identification of themes and subcategories. RESULTS: Mental health (MH) emerged as an overarching health concern with three major themes i.e. appraisal of the mental burden (extent and general susceptibility), stress-inducing factors, and coping strategies. Many participants agreed that MH did not become a concern to them until after immigration. Women discussed their compromised MH using verbal and symptomatic expressions. The stress-inducing factors identified by participants included loss of social support, economic uncertainties, downward social mobility, mechanistic lifestyle, barriers in accessing health services, and climatic and food changes. Women's major coping strategies included increased efforts to socialize, use of preventative health practices and self-awareness. CONCLUSION: Although participant women discussed a number of ways to deal with post-immigration stressors, the women's perceived compromised mental health reflects the inadequacy of their coping strategies and the available resources. Despite access to healthcare providers, women failed to identify healthcare encounters as opportunities to seek help and discuss their mental health concerns. Health and social care programs need to actively address the compromised mental health perceived by the studied group.

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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.415
Threshold uncertainty score0.832

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.013
GPT teacher head0.321
Teacher spread0.308 · 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

Citations192
Published2005
Admission routes3
Has abstractyes

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