Exploring opportunities to strengthen rural tuberculosis health service delivery: a qualitative study with health workers in Tibet autonomous region, China
Bibliographic record
Abstract
OBJECTIVES: This qualitative study aimed to explore opportunities to strengthen tuberculosis (TB) health service delivery from the perspectives of health workers providing TB care in Shigatse prefecture of Tibet Autonomous Region, China. DESIGN: Qualitative research, semi-structured in-depth interviews. SETTING: The TB care ecosystem in Shigatse, including primary and community care. PARTICIPANTS: Participants: 37 semi-structured interviews were conducted with village doctors (14), township doctors and nurses (14), county hospital doctors (7) and Shigatse Centre for Disease Control staff (2). RESULTS: The three main themes reported include (1) the importance of training primary and community health workers to identify people with symptoms of TB, ensure TB is diagnosed and link people with TB to further care; (2) the need to engage community health workers to ensure retention in care and adherence to TB medications; and (3) the opportunity for innovative technologies to support coordinated care, retention in care and adherence to medication in Shigatse. CONCLUSIONS: The quality of TB care could be improved across the care cascade in Tibet and other high-burden, remote settings by strengthening primary care through ongoing training, greater support and inclusion of community health workers and by leveraging technology to create a circle of care. Future formative and implementation research should include the perspectives of health workers at all levels to improve care organisation and delivery.
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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 | 0.009 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".