Mental health needs and services for migrants: an overview for primary care providers
Bibliographic record
Abstract
BACKGROUND: The objective of this article is to present an overview of the burden, spectrum of diseases and risk factors for mental illness among subgroups of migrants, namely, immigrants, refugees and individuals with precarious legal status. This expert review summarises some of the implications for primary care services in migrant receiving countries in the global North. METHODS: A broad literature review was conducted on the epidemiology of mental health disorders in migrants and the available evidence on mental health services for this population focusing on key issues for primary care practitioners in high-income countries. RESULTS: Although most migrants are resilient, migration is associated with an over-representation of mental disorder in specific subpopulations. There is a general consensus that stress-related disorders are more prevalent among refugee populations of all ages compared to the general population. Relative to refugees, migrants with precarious legal status may be at even higher risk of depression and anxiety disorders. Persistence and severity of psychiatric disorders among migrant populations can be attributed to a combination of factors including severity of trauma exposures during the migration process. Exposure to stressors after resettlement, such as poverty and limited social support, also impacts mental illness. Services for migrants are affected by restricted accessibility and should address cultural and linguistic barriers to and issues in the larger social environment that impact psychosocial functioning. CONCLUSION: There is substantial burden of mental illness among some migrant populations. Primary care providers seeking to assist individuals need to be cognizant of language barriers to and challenges of working with interpreters as well as sensitive to cultural and social contexts within the diagnosis and service delivery process. In addition, best practices in screening migrants and providing intervention services for mental disorders need to be sensitive to where individuals and families are in the resettlement trajectory.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".