Reframing resilience: Strengthening continuity of patient care to improve the mental health of immigrants and refugees
Bibliographic record
Abstract
Refugee and immigrant populations experience many pre- and post-migration risk factors and stressors that can negatively impact their mental health. This qualitative study aimed to explore the system-level issues that affect the access to, as well as quality and outcomes of mental health care for immigrants and refugees, with a particular focus on challenges in the continuity of patient care. A multidisciplinary group of health providers, including nurses, identified six themes including (i) perceived access to care; (ii) coordination amongst health care providers; (iii) patient connections with community organizations; (iv) coordinated care planning; (v) organizational protocols, policies and procedures and (vi) systemic and health care training needs. Although patient resilience is seen as a pivotal way for vulnerable populations to cope with hardship, there is a clear need for creating a resilient health care system that is able to anticipate and adapt to adverse situations. The findings from this study have implications for nurses, who are uniquely positioned to advocate for public health policy that improves the continuity of health care by creating systemic resilience.
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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 | 0.013 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.010 | 0.009 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.002 | 0.004 |
| 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".