Developing a Collaborative Approach to Support Access and Acceptability of Mental Health Care for Refugee Youth: An Exploratory Case Study with Young Afghan Refugees
Bibliographic record
Abstract
Despite an increased prevalence of psychiatric morbidity, minor refugees resettled in Western host societies are less likely to access mental health care services than their native peers. This study aims to explore how a collaborative approach can be implemented to promote access to specialized mental health care. Collaborative mental health care embeds specialized intervention in primary care settings and emphasizes the inclusion of minority cultural perspectives through an interdisciplinary, intersectoral network. In this study, we analyze how such a collaborative approach can support access to specialized mental health care for refugee youth. The study presents findings from a qualitative multiple-case study (n = 10 refugee patients), conducted in the setting of a psychiatric day program for young refugees that develops an intersectional, collaborative practice in supporting minor refugees’ trajectory from referral to admission. Building on in-depth interviews, participant observation and case documents, within-case analysis and cross-case inductive thematic analysis identify the specific working mechanisms of a collaborative approach. The results indicate how this intersectoral approach addresses the interplay between traumatic suffering and both cultural and structural determinants of mental health. To conclude, a discussion identifies future research directions that may further strengthen the role of collaborative practice in promoting mental health care access for refugee youth.
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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 | 0.011 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.021 | 0.009 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.003 | 0.003 |
| 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".