A knowledge translation intervention to improve tuberculosis care and outcomes in Malawi: a pragmatic cluster randomized controlled trial
Bibliographic record
Abstract
BACKGROUND: Lay health workers (LHWs) play a pivotal role in addressing the high TB burden in Malawi. LHWs report lack of training to be a key barrier to their role as TB care providers. Given the cost of traditional off-site training, an alternative approach is needed. Our objective was to evaluate the effectiveness of a KT intervention tailored to LHWs needs. METHODS: The study design is a pragmatic cluster randomized trial. The study was embedded within a larger trial, PALMPLUS, and compared three arms which included 28 health centers in Zomba district, Malawi. The control arm included 14 health centers randomized as controls in the larger trial and maintained as control sites. Seven of 14 PALMPLUS intervention sites were randomized to the LHW intervention (PALM/LHW intervention arm), and the remaining 7 PALMPLUS sites maintained as a PALM only arm. PALMPLUS intervention sites received an educational outreach program targeting mid-level health workers. LHW intervention sites received both the PALMPLUS intervention and the LHW intervention employing on-site peer-led educational outreach and a point-of-care tool tailored to LHWs identified needs. Control sites received no intervention. The main outcome measure is the proportion of treatment successes. RESULTS: Among the 28 sites, there were 178 incident TB cases with 46/80 (0.58) successes in the control group, 44/68 (0.65) successes in the PALMPLUS group, and 21/30 (0.70) successes in the PALM/LHW intervention group. There was no significant effect of the intervention on treatment success in the univariate analysis adjusted for cluster randomization (p = 0.578) or multivariate analysis controlling for covariates with significant model effects (p = 0.760). The overall test of the intervention-arm by TB-type interaction approached but did not achieve significance (p = 0.056), with the interaction significant only in the control arm [RR of treatment success for pulmonary TB relative to non-pulmonary TB, 1.18, 95% CI 1.05-1.31]. CONCLUSIONS: We found no significant treatment effect of our intervention. Given the identified trend for effectiveness and urgent need for low-cost approaches to LHW training, further evaluation of tailored KT strategies as a means of LHW training in Malawi and other LMICs is warranted. TRIAL REGISTRATION: ClinicalTrials.gov NCT01356095 .
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".