A mixed methods evaluation of a differentiated care model piloted for TB care in south India
Bibliographic record
Abstract
Background: India's National TB Elimination Program emphasizes patient-centered care to improve TB treatment outcomes. We describe the lessons learned from the implementation of a differentiated care model for TB care among individuals diagnosed with active TB. Design and methods: Used mixed methods to pilot the Differentiated Care Model. Community health workers (CHWs) conducted a risk and needs assessment among individuals who were recently began TB treatment. Individuals identified with specific factors that are associated with poor treatment adherence were provided education, counseling, and linked to treatment and support services. Examined changes in TB treatment outcomes between the two cohorts of individuals on TB treatment before and after the intervention. We used qualitative research methods to explore the experiences of patients, family members, and front-line TB workers with the implementation of the DCM pilot. Results: The CHWs were adept at the identification of individuals with risks to non-adherence. However, only a few provided differentiated care, as envisioned. There was no significant change in the TB treatment outcomes between the two cohorts of patients examined. CHWs' ability to provide differentiated care on a scale was limited by the short duration of implementation, their inadequate skills to manage co-morbidities, and the suboptimal support at the field level. Conclusions: It is feasible for a cadre of well-trained front-line workers, mentored and supported by counselors and doctors, to provide differentiated care to those at risk for unfavorable TB treatment outcomes. However, differentiated care must be implemented on a scale for a duration that allows a change from the conventional practice of front-line workers, in order to influence the outcomes of population-level 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 | 0.039 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".