Impact of peer-trainer leadership style on uptake of a peer led educational outreach intervention to improve tuberculosis care and outcomes in Malawi: a qualitative study
Bibliographic record
Abstract
Abstract Background Little is known about how to build leadership capacity to support implementation of evidence-based practices within health systems. We observed substantial variability across sites in uptake and sustainability of a peer-led educational outreach intervention for lay health workers (LHWs) providing tuberculosis care in Malawi. Feedback from peer-trainers (PTs) suggested that leadership may have contributed to the variation. We sought to assess the impact of PT leadership style on implementation, and to identify leadership traits of more successful PTs, to inform future implementation planning and to identify targets for leadership capacity building. Methods Qualitative study employing interviews with PTs and LHWs at high and low implementation sites, and review of study team and quarterly PT meeting notes. High implementation sites achieved high uptake, sustainability and fidelity of implementation including: close adherence to training content and process, high levels of coverage (training most or all eligible LHWs at their site), and outcomes were achieved with high levels of self reported competence with the intervention among both PTs and LHWs. Low implementation sites achieved limited coverage (<= 50% of LHWs trained), and intervention fidelity. Results Eight PTs and 10 LHWs from eight high and 10 low implementation sites participated in interviews. Leadership traits of more successful PTs included: flexibility in their approach to training, role modeling and provision of supportive supervision to support learning; addressing challenges proactively and as they occurred; collaborative planning; knowledgeable; and availability to support implementation. Traits unique to less successful PTs included: a poor attitude toward their role as PT and a passive-avoidant approach to challenges. Conclusion This study identified leadership traits more common among unit level leaders at sites with higher uptake, sustainability, and fidelity of implementation. These findings provide a starting point for development and evaluation of a leadership capacity building intervention for unit level leaders to support implementation.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| 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".