Development and Evaluation of a Tailored Knowledge Translation Intervention to Improve Lay Health Workers Ability to Effectively Support TB Treatment Adherence in Malawi
Bibliographic record
Abstract
Background: Task shifting provision of basic health care services to lay health workers (LHWs) is increasingly employed to combat the global shortage of skilled health workers, particularly in low and middle income countries, where the shortage is greatest. Despite evidence for the effectiveness of LHWs in improving access to basic health services and positively impacting a variety of health outcomes, questions remain as to how recognized weaknesses in training and supervision are best addressed. This thesis employed a sequential-concurrent mixed-methods design and is composed of 3 studies with the objective of designing and rigorously evaluating a knowledge translation (KT) intervention tailored to address identified barriers to LHWs ability to function optimally as TB adherence supporters in Zomba district, Malawi. \n\nMethods & Findings: The first study utilized the qualitative methods of focus groups and interviews conducted with LHWs routinely involved in provision of care to TB patients. Lack of TB knowledge and job-specific training were identified as the key barriers to LHWs in their role as TB adherence supporters. Based on these findings, a KT intervention was developed and tailored to the identified training gaps. The second study, evaluated the effectiveness of the intervention in improving TB treatment adherence in a cluster randomized controlled trial, which showed no evidence for effectiveness of the intervention. The third study, conducted concurrently with the cluster trial, employed qualitative interviews to explore LHWs experiences with the intervention to identify \nii\naspects of the intervention found to be helpful and areas in need of improvement. Study 3 found that the intervention was well received and valued, with reported benefits to LHWs through improved knowledge and skills, and increased confidence. Suggestions for improvement varied considerably, with an anticipated concern with the lack of stipends and conduct of training on-site, raised as an issue by a minority of participants. \n\nConclusion: This thesis suggests that a multi-component KT strategy tailored to address local barriers, was well received and valued by LHWs, and may represent a cost-effective approach to LHW training. However, given the trend for effectiveness did not reach significance in this underpowered study, further research is needed.
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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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".