The social determinants of migrant domestic worker (MDW) health and well-being in the Western Pacific Region: A Scoping Review
Bibliographic record
Abstract
The health and well-being of transnational migrant domestic workers (MDWs) is a pressing but largely neglected public health concern. The Asia Pacific region is home to over 20% of the global MDW population. Living and working conditions, social contexts, political environments, and migration regimes are recognized as consequential to the health of this population, but currently no synthesis of available literature to prioritize research or policy agenda setting for MDW has yet been conducted. This scoping review screened 6,006 peer-reviewed articles and 1,217 gray literature sources, identifying 173 articles and 276 gray literature sources that reported key MDW health outcomes, social determinants of health, and related interventions. The majority of identified studies were observational and focused on the prevalence of common mental disorders and chronic physical conditions, with most studies lacking population representativeness. Identified social determinants of health were primarily concerned with personal social and financial resources, and health knowledge and behaviors, poor living and working conditions, community resources, experienced stigma and discrimination, poor healthcare access, exploitation within the MDW employment industry, and weak governance. Six interventional studies were identified that targeted individual-level health determinants such as financial and health knowledge with mixed effectiveness. Future population representative epidemiological and respondent driven sampling studies are needed to estimate population health burdens. In addition, randomized control trials and public health intervention studies are needed to improve women's health outcomes and address proximal health determinants to reduce health inequalities. Leveraging social networks and community facing non-governmental organizations (NGOs) are promising directions to overcome access to care for this population.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 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".