A process evaluation of an eHealth intervention to strengthen the circle of tuberculosis care in Shigatse, Tibet, China
Bibliographic record
Abstract
Tuberculosis (TB) is an ongoing global health threat that has been exacerbated by the COVID-19 pandemic. People with TB need comprehensive medical and social supports to ensure they can maintain and complete TB treatment. TB programs globally have turned to eHealth to bridge gaps in access and strengthen the circle of care around people with TB. This study evaluates the implementation of an intervention aimed at improving TB care in Shigatse, using the CFIR framework to identify factors influencing its success. The intervention included the use of e-Monitor boxes and WeChat for patient engagement. Data were collected through interviews with patients, treatment supporters, and health workers. Key challenges identified included inadequate infrastructure, digital literacy barriers, and unclear roles due to recent TB service delivery reforms. Enablers included strong social structures, proximity to village doctors, and government support for free TB treatment. Results showed that while older patients faced difficulties with digital tools, younger family members often assisted, enhancing engagement. Health workers' training and the timing of training sessions were critical to the intervention's success. The study concludes that despite challenges, the intervention was generally well-received and effective, with recommendations for ongoing training and adaptation to local contexts.
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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 | 0.017 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".