Coping and resilience among ethnoracial individuals experiencing homelessness and mental illness
Bibliographic record
Abstract
BACKGROUND: The multiple challenges that ethnoracial homeless individuals experiencing mental illness face are well documented. However, little is known about how this homeless subpopulation copes with the compounding stressors of racial discrimination, homelessness and mental illness. AIMS: This study is an in-depth investigation of the personal perceived strengths, attitudes and coping behaviors of homeless adults of diverse ethnoracial backgrounds experiencing homelessness and mental illness in Toronto, Canada. METHOD: Using qualitative methods, 36 in-depth semi-structured interviews were conducted to capture the perspectives of ethnoracial homeless participants with mental illness on coping and resilience. Transcripts were analyzed using thematic analysis. RESULTS: Similar to prior findings in the general homeless population, study participants recognized personal strengths and attitudes as great sources of coping and resilience, describing hope and optimism, self-esteem and confidence, insight into their challenges and spirituality as instrumental to overcoming current challenges. In addition, participants described several coping strategies, including seeking support from family, friends and professionals; socializing with peers; engaging in meaningful activities; distancing from overwhelming challenges; and finding an anchor. CONCLUSION: Findings suggest that homeless adults with mental illness from ethnoracial groups use similar coping strategies and sources of resilience with the general homeless population and highlight the need for existing services to foster hope, recognize and support individual coping strategies and sources of resilience of homeless individuals experiencing complex challenges.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".