Mental Health and Wellbeing of Rohingya Refugees: A Scoping Review
Bibliographic record
Abstract
Since 1978, the Rohingya population has faced multiple humanitarian crises, including racial disparities, which significantly escalated during 2017, leading to widespread exile from Myanmar. Reportedly, violence, persecution, and trauma have posed grievous impacts on the mental health of these forcibly displaced people. This scoping review aimed to synthesize the evidence regarding the overall epidemiologic burden of psychological problems of Rohingya refugees with their associated factors. We evaluated five major databases and additional sources till July 31, 2024, and included articles according to the eligibility criteria following the Joanna Briggs Institute (JBI) guidelines and the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) checklist. Out of 373 citations retrieved from multiple sources, we included 35 articles in this review. Most of the articles reported a high prevalence of different psychological symptoms of Rohingya refugees, such as depression, anxiety, post-traumatic stress disorder, persistent complex bereavement disorder, feeling afraid, etc. Several correlates of mental health problems were reported, including older age, being female, illiteracy, experiences of torture, sexual violence, unemployment, food insecurity, statelessness, lack of healthcare access, unhygienic campsites, and preexisting health problems, etc. There were significant gaps in community-level intervention studies, however, Group Integrated Adapt Therapy (IAT-G) and Mental Health and Psychosocial Support (MHPSS) services are widely used. The available evidence suggests a huge burden of mental health disorders with several biopsychosocial factors that may assist in better policymaking and implementation of multilayered approaches, like improving healthcare access, training healthcare providers, more community-based intervention studies, and introducing tele-mental health services for Rohingya refugees.
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 | 0.006 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".