Process evaluation of an implementation strategy to support uptake of a tuberculosis treatment adherence intervention to improve TB care and outcomes in Malawi
Bibliographic record
Abstract
OBJECTIVE: To assess implementation and to identify barriers and facilitators to implementation, sustainability and scalability of an implementation strategy to provide lay health workers (LHWs) with the knowledge, skills and tools needed to implement an intervention to support patient tuberculosis (TB) treatment adherence. DESIGN: Mixed-methods design including a cluster randomised controlled trial and process evaluation informed by the RE-AIM framework. SETTING: Forty-five health centres (HCs) in four districts in the south east zone of Malawi, who had an opportunity to receive cascade training. PARTICIPANTS: Forty-five peer-trainers (PTs), 23 patients and 20 LHWs. INTERVENTION: Implementation strategy employing peer-led educational outreach, a clinical support tool and peer support network to implement a TB treatment adherence intervention. OUTCOME MEASURES: Process data were collected from study initiation to the end-of-study PT meeting, and included: LHW and patient interviews, quarterly PT meeting notes, training logs and study team observations and meeting notes. Data sources were first analysed in isolation, followed by method, data source and analyst triangulation. Analyses were conducted independently by two study team members, and themes revised through discussion and involvement of additional study team members as needed. RESULTS: Forty-one HCs (91%) trained at least one LHW. Of 256 LHWs eligible to participate at study start 152 (59%) completed training, with the proportion trained per HC ranging from 0% to 100% at the end of initial cascade training. Lack of training incentives was the primary barrier to implementation, with intrinsic motivation to improve knowledge and skills, and to improve patient care and outcomes the primary facilitators of participation. CONCLUSION: We identified important challenges to and potential facilitators of implementation, scalability and sustainability, of the TB treatment adherence intervention. Findings provide guidance to scale-up, and use of the implementation strategies employed, to address LHW training and supervision in other areas. TRIAL REGISTRATION NUMBER: NCT02533089.
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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 | 0.078 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 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".