Access to free or low-cost tuberculosis treatment for migrants and refugees along the Thailand-Myanmar border in Tak province, Thailand
Bibliographic record
Abstract
BACKGROUND: In Tak province, Thailand migrants and refugees from Myanmar navigate a pluralistic healthcare system to seek Tuberculosis (TB) care from a variety of government and non-governmental providers. This multi-methods qualitative study examined access to TB, TB/HIV and multidrug-resistant tuberculosis (MDR-TB) treatment with an emphasis on barriers to care and enabling factors. METHODS: In the summer and fall of 2014, we conducted 12 key informant interviews with public health officials and TB treatment providers. We also conducted 11 focus group discussions with migrants and refugees who were receiving TB, TB/HIV and MDR-TB treatment in Tak province as well as non-TB patients. We analyzed these data through thematic analysis using both predetermined and emergent codes. As a second step in the qualitative analysis, we explored the barriers and enabling factors separately for migrants and refugees. RESULTS: We found that refugees face fewer barriers to accessing TB treatment than migrants. For both migrants and refugees, legal status plays an important intermediary role in influencing the population's ability to access care and eligibility for treatment. Our results suggest that there is a large geographical catchment area for migrants who seek TB treatment in Tak province that extends beyond provincial boundaries. Migrant participants described their ability to seek care as linked to the financial and non-financial resources required to travel and undergo treatment. Patients identified language of health services, availability of free or low cost services, and psychosocial support as important health system characteristics that affect accessibility. CONCLUSION: Access to TB treatment for migrants and refugees occurs at the interface of health system accessibility, population ability and legal status. In Tak province, migrant patients draw upon their social networks and financial resources to navigate a pathway to treatment. We revised a conceptual framework for access to healthcare to incorporate legal status and the cyclical pathways through which migrants access TB treatment in this region. We recommend that organizations continue to collaborate to provide supportive services that help migrants to access and continue TB treatment.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".