Including undocumented migrants in universal health coverage: a maternal health case study from the Thailand-Myanmar border
Bibliographic record
Abstract
BACKGROUND: Many countries aspiring to achieve universal health coverage struggle with how to ensure health coverage for undocumented migrants. Using a case study of maternal health care in a Thailand-Myanmar border region this article explores coverage for migrants, service provision challenges and the contribution of a voluntary health insurance program. METHODS: In 2018 we interviewed 18 key informants who provided, oversaw or contributed to maternal healthcare services for migrant women in the border region of Tak province, Thailand. RESULTS: In this region, we found that public and non-profit providers helped increase healthcare coverage beyond undocumented migrants' official entitlements. Interview participants explained that Free and low-cost antenatal care (ANC) is provided to undocumented migrants through migrant specific clinics, outreach programs and health posts. Hospitals offer emergency birth care, although uninsured migrant patients are subsequently billed for the services. Care providers identified sustainability, institutional debt from unpaid obstetric hospital bills, cross border logistical difficulties and the late arrival of patients requiring emergency lifesaving interventions as challenges when providing care to undocumented migrants. An insurance fund was developed to provide coverage for costly emergency interventions at Thai government hospitals. The insurance fund, along with existing free and low-cost services, helped increase population coverage, range of services and financial protection for undocumented migrants. CONCLUSIONS: This case study offers considerations for extending health coverage to undocumented populations. Non-profit insurance funds can help to improve healthcare entitlements, provide financial protection and reduce service providers' debt. However, there are limits to programs that offer voluntary coverage for undocumented migrants. High costs associated with emergency interventions along with gaps in insurance coverage challenge the sustainability for NGO, non-profit and government health providers and may be financially disastrous for patients. Finally, in international border regions with high mobility, it may be valuable to implement and strengthen cross border referrals and health insurance for migrants.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".