If I Was Going to Kill Myself, I Wouldn't Be Calling You. I am Asking for Help: Challenges Influencing Immigrant and Refugee Women's Mental Health
Bibliographic record
Abstract
It is estimated that 37% of Canadians experience some types of mental health problem. As a result of the migration process, many immigrant and refugee women suffer serious mental illness such as depression, schizophrenia, posttraumatic stress disorder, suicide, and psychosis. The purpose of this exploratory qualitative study, informed by the ecological conceptual framework and postcolonial feminist perspectives, was to increase understanding of the mental health care experiences of immigrant and refugee women by acquiring information regarding factors that either support or inhibit coping. Ten women (five born in China and five born in Sudan) who were living with mental illness were interviewed. Analysis revealed that (a) women's personal experience with biomedicine, fear, and lack of awareness about mental health issues influences how they seek help to manage mental illness; (b) lack of appropriate services that suit their needs are barriers for these women to access mental health care; and (c) the women often draw upon informal support systems and practices and self-care strategies to cope with their mental illnesses and its related problems. The authors discuss implications for practice and make recommendations for intervention strategies that will facilitate women's mental health care and future research.
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 | 0.004 | 0.006 |
| 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 | 0.010 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".