Qualitative cross-sectional study of the perceived causes of depression in South Asian origin women in Toronto
Bibliographic record
Abstract
Objective: To explore how South Asian origin women in Toronto, Canada, understand and explain the causes of their depression. Design: Cross-sectional in-depth qualitative interviews. Setting: Outpatient service in Toronto, Ontario. Participants: Ten women with symptoms of depression aged between 22 and 65 years of age. Seven were from India, two from Sri Lanka and one from Pakistan. Four were Muslim, three Hindu and three Catholic. Two participants had university degrees, one a high school diploma and seven had completed less than a high school education. Eight were married, one was unmarried and one a widow. Results: Three main factors emerged from the participant narratives as the causes of depression: family and relationships, culture and migration and socioeconomic. The majority of the participants identified domestic abuse, marital problems and interpersonal problems in the family as the cause of their depression. Culture and migration and socioeconomic factors were considered contributory. None of our study participants reported spiritual, supernatural or religious factors as causes of depression. Conclusion: A personal-social-cultural model emerged as the aetiological paradigm for depression. Given the perceived causation, psycho-social treatment methods may be more acceptable for South Asian origin 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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".