HEALTH CARE PROVIDERS' PERSPECTIVE OF THE GENDER INFLUENCES ON IMMIGRANT WOMEN'S MENTAL HEALTH CARE EXPERIENCES
Bibliographic record
Abstract
The number of immigrants coming to Canada has increased in the last three decades. It is well documented that many immigrant women suffer from serious mental health problems such as depression, schizophrenia, and post migration stress disorders. Evidence has shown that immigrant women experience difficulties in accessing and using mental health services. Informed by the post-colonial feminist perspective, this qualitative exploratory study was conducted with seven health care providers who provide mental health services to immigrant women. In-depth interviews were used to obtain information about immigrant women's mental health care experiences. The primary goal was to explore how contextual factors intersect with race, gender, and class to influence the ways in which immigrant women seek help and to increase awareness and understanding of what would be helpful in meeting the mental health care needs of the immigrant women. The study's results reveal that (a) immigrant women face many difficulties accessing mental health care due to insufficient language skills, unfamiliarity/unawareness of services, and low socioeconomic status; (b) participants identified structural barriers and gender roles as barriers to accessing the available mental health services; (c) the health care relationship between health care providers and women had profound effects on whether or not immigrant women seek help for mental health problems.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.007 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".