A mental health framework from the voices of refugees
Bibliographic record
Abstract
BACKGROUND: Refugee groups fleeing war and violence and resettling in the West are one of the population groups that are poorly understood. Understanding their mental health challenges and providing effective and evidence-based interventions continue to be formidable challenges. AIM: This study presents a refugee mental health framework [RMHF] that was developed to address the gaps in understanding and responding to the needs of refugee populations by prioritizing their voices, and incorporating lessons learned from working with these refugee communities into the development of the framework. METHOD: A RMHF was developed, presented and refined with input from refugee communities, multiple stakeholders and an expert panel. RESULTS/CONCLUSIONS: This paper presents the process and finalized framework, and discusses its utility as a mapping, planning and intervention tool in supporting refugee communities with their resettlement and promoting mental wellbeing.
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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 | 0.025 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.014 | 0.018 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.004 | 0.007 |
| 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".