Refugee mental health and human rights: A challenge for global mental health
Bibliographic record
Abstract
that presents recent work that deepens our understanding of the refugee experience-from the forces of displacement, through the trajectory of migration, to the challenges of resettlement. Mental health research on refugees and asylum seekers has burgeoned over the past two decades with epidemiological studies, accounts of the lived experience, new conceptual frameworks, and advances in understanding of effective treatment and intervention. However, there are substantial gaps in available research, and important ethical and methodological challenges. These include: the need to adopt decolonizing, participatory methods that amplify refugee voices; the further development of frameworks for studying the broad impacts of forced migration that go beyond posttraumatic stress disorder; and more translational research informed by longitudinal studies of the course of refugee adaptation. Keeping a human rights advocacy perspective front and center will allow researchers to work in collaborative ways with both refugee communities and receiving societies to develop innovative mental health policy and practice to meet the urgent need for a global response to the challenge of forced migration, which is likely to grow dramatically in the coming years as a result of the impacts of climate change.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.023 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.021 | 0.036 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".