Prevalence of common mental health issues among migrant workers: A systematic review and meta-analysis
Bibliographic record
Abstract
Previous literature has shown that migrant workers manifested higher common mental issues (especially depressive symptom) compared to local workers due to stressors such as financial constraint and lack of access to healthcare. The aim of this systematic review and meta-analysis is to summarize the current body of evidence for the prevalence of depression and anxiety among migrant workers as well as exploring the risk factors and the availability of social support for migrant workers. Seven electronic databases, grey literature and Google Scholar were searched for studies from 2015 to 2021 related to mental health, social support and migrant workers. Study quality was assessed using the Newcastle Ottawa Scale and the Joanna Briggs Institute Qualitative Assessment and Review Instrument (JBI-QARI). Study heterogeneity was evaluated using I2 statistics. Random effects meta-analysis results were presented given heterogeneity among studies. The search returned 27 articles and only seven studies were included in meta-analysis, involving 44 365 migrant workers in 17 different countries. The overall prevalence of depression and anxiety among migrant workers was 38.99% (95% CI = 0.27, 0.51) and 27.31% (95% CI = 0.06, 0.58), respectively. Factors such as age, biological (health issue, family history of psychiatric disorder), individual (poor coping skills), occupational (workplace psychosocial stressors, poor working condition, salary and benefits issue, abuse), environmental (limited access towards healthcare, duration of residence, living condition) and social factor (limited social support) were associated with a mental health outcome in migrant workers. The availability of social support for migrant workers was mainly concentrated in emotional type of support. A high prevalence of depression and anxiety was found among migrant workers across the globe. This finding warrants a collective effort by different parties in providing assistance for migrant workers to promote their mental well-being.
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 | 0.013 | 0.032 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.036 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".