Internal Migration as a Social Determinant of Occupational Health and WASH Access in Myanmar
Bibliographic record
Abstract
Background: Migration is at an all-time high worldwide, and despite increased focus on international migrants, there is little evidence about internal migrants' exposures to socioeconomic, occupational, and environmental risk factors in low-and middle-income countries. Objective: The aim of this study was to examine differences in occupational health and access to water, sanitation, and hygiene (WASH) between internal migrants and non-migrants. Methods: A face-to-face survey (n = 937) was conducted in Mandalay, Myanmar. Bivariate and multivariate analysis included traditional social determinants such as education, income, occupation, gender, age, and location in addition to internal migration status. Findings: The majority of internal migrants (23% of the total sample) were labor migrants (67.3%), and while common social determinants (e.g., household income, education, and gender) were not statistically different between migrants and non-migrants, these groups reported different occupational profiles (p < 0.001). Migrants had higher odds of being street vendors (AOR = 2.26; 95% CI 1.33-3.85; p = 0.003) and were less likely to work labor jobs such as in factories or construction (AOR = 0.44; 95% CI 0.19-1.00; p = 0.051) when controlling for age, gender, education, and location. Internal migrants had significantly greater probabilities of experiencing some injuries and illness symptoms, such as cuts, vomiting, coughing, heatstroke, and diarrhea at work (p < 0.001). Compared to non-migrants, migrants' households were approximately three times more likely (AOR = 3.45; 95% CI 2.17-5.62; p < 0.001) to have an unimproved source of drinking water and twice as likely (AOR = 1.98; 95% CI 1.10-3.58; p < 0.05) to have unimproved sanitation facilities in their homes. Conclusions: The results underscore the importance of considering internal migration as an aspect of social determinants analyses, and the need for targeting appropriate WASH interventions to address inequities.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".