Migrant and Refugee Patient Perspectives on Travel and Tuberculosis along the Thailand-Myanmar Border: A Qualitative Study
Bibliographic record
Abstract
BACKGROUND: The Thailand-Myanmar border separates two very different health systems. The healthcare system in eastern Myanmar remains underdeveloped as a result of decades of instability. Comparatively, Tak province, Thailand has more healthcare resources. In this Thai border province government hospitals and non-governmental organizations provide tuberculosis (TB) treatment to migrants and refugees. OBJECTIVES: Our overall study aimed to explore accessibility of TB treatment, TB surveillance and health system responsiveness specific to migrant and refugee populations in Tak province. In this paper, we focus on the perspectives of migrant and refugee TB patients with respect to travel and treatment in Tak province. METHODS: In 2014 we conducted focus group discussions with 61 TB, Tuberculosis and Human Immunodeficiency Virus co-infection, and multidrug-resistant TB patients in Tak province. We analyzed the data for content and themes and documented individual travel trajectories. RESULTS AND DISCUSSION: Migrants are travelling with active TB within the country and between Thailand and Myanmar. Migrants primarily travelled to obtain treatment but two participants reported travelling home to seek family care in Myanmar before returning to Thailand for treatment. Travel, while expensive and arduous, is an adaptive strategy that migrants use to access healthcare. CONCLUSIONS: Migrant's need for travel points to larger difficulties associated with healthcare access in the border region. Long distance travel with an infectious disease can be seen as an indicator that local healthcare is not available or affordable. These findings suggest that public health officials from both sides of the border should discuss the factors that contribute to travel with active TB and explore potential solutions to mitigate disease transmission in migrant populations.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".