The impact of a knowledge translation intervention employing educational outreach and a point-of-care reminder tool vs standard lay health worker training on tuberculosis treatment completion rates: study protocol for a cluster randomized controlled trial
Bibliographic record
Abstract
BACKGROUND: Despite availability of effective treatment, tuberculosis (TB) remains an important cause of morbidity and mortality globally, with low- and middle-income countries most affected. In many such settings, including Malawi, the high burden of disease and severe shortage of skilled healthcare workers has led to task-shifting of outpatient TB care to lay health workers (LHWs). LHWs improve access to healthcare and some outcomes, including TB completion rates, but lack of training and supervision limit their impact. The goals of this study are to improve TB care provided by LHWs in Malawi by refining, implementing, and evaluating a knowledge translation strategy designed to address a recognized gap in LHWs' TB and job-specific knowledge and, through this, to improve patient outcomes. METHODS/DESIGN: We are employing a mixed-methods design that includes a pragmatic cluster randomized controlled trial and a process evaluation using qualitative methods. Trial participants will include all health centers providing TB care in four districts in the South East Zone of Malawi. The intervention employs educational outreach, a point-of-care reminder tool, and a peer support network. The primary outcome is proportion of treatment successes, defined as the total of TB patients cured or completing treatment, with outcomes taken from Ministry of Health treatment records. With an alpha of 0.05, power of 0.80, a baseline treatment success of 0.80, intraclass correlation coefficient of 0.1 based on our pilot study, and an estimated 100 clusters (health centers providing TB care), a minimum of 6 patients per cluster is required to detect a clinically significant 0.10 increase in the proportion of treatment successes. Our process evaluation will include interviews with LHWs and patients, and a document analysis of LHW training logs, quarterly peer trainer meetings, and mentorship meeting notes. An estimated 10-15 LHWs and 10-15 patients will be required to reach saturation in each of 2 planned interview periods, for a total of 40-60 interview participants. DISCUSSION: This study will directly inform the efforts of knowledge users within TB care and, through extension of the approach, other areas of care provided by LHWs in Malawi and other low- and middle-income countries. TRIAL REGISTRATION: ClinicalTrials.gov NCT02533089 . Registered 20 August 2015. Protocol Date/Version 29 May 2016/Version 2.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.018 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".