Mental health care utilisation and access among refugees and asylum seekers in Europe: A systematic review
Bibliographic record
Abstract
BACKGROUND: Refugees and asylum seekers often have increased mental health needs, yet may face barriers in accessing mental health and psychosocial support (MHPSS) services in destination countries. The aim of this systematic review is to examine evidence on MHPSS service utilisation and access among refugees and asylum seekers in European Union Single Market countries. METHODS: Four peer-reviewed and eight grey literature databases were searched for quantitative and qualitative literature from 2007 to 2017. Access was categorised according to Penchansky and Thomas' framework and descriptive analyses were conducted. Quality of studies was assessed by the Newcastle-Ottawa scale and the Critical Appraisal Skills Programme checklist. RESULTS: Twenty-seven articles were included. The findings suggest inadequate MHPSS utilisation. Major barriers to accessing care included language, help-seeking behaviours, lack of awareness, stigma, and negative attitudes towards and by providers. CONCLUSIONS: Refugees and asylum seekers have high mental health needs but under-utilise services in European host countries. This underutilisation may be explained by cultural-specific barriers which need to be tackled to increase treatment demand. Training health providers on cultural models of mental illness may facilitate appropriate identification, referral, and care. Based on these findings, it is crucial to review policies regarding MHPSS provision across the EU.
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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 | 0.006 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".