Jumping into the unkown: the stress of female newcomers' adjustment to life in Toronto and their implications for delivering primary mental health care
Bibliographic record
Abstract
Following migration to Canada, female newcomers must endure a process of resettlement that often is accompanied by considerable stress, creating vulnerability towards related disorders and mental ill-health. Due to socio-cultural and linguistic barriers, women are more likely than men to experience feelings of isolation and distress during this resettlement process. Therefore, it is important to ensure that mental health care is accessible for them. This study examines sources of mental stress and distress female newcomers experience in adjusting to a new place, Toronto, and to a new health care system. Importantly, this adjustment is framed as a process that occurs over time and place. As part of a larger community-based participatory action research study, this analysis draws upon 30 semi-structured interviews conducted with female newcomers from 4 cultural-linguistic groups. Thematic findings indicate major sources of stress in adjusting to life in Toronto include: navigation, concerns regarding personal safety, adapting to a new lifestyle, and finding and maintaining employment. In adjusting to Canada's health care system, sources of stress include: learning how to access care; not having access to specialists; and adapting to a new culture of care. Conclusions consider implications of these findings for delivering primary mental health care.
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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 | 0.001 | 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 | 0.012 | 0.006 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".