How does the effectiveness of strategies to improve healthcare provider practices in low-income and middle-income countries change after implementation? Secondary analysis of a systematic review
Bibliographic record
Abstract
BACKGROUND: A recent systematic review evaluated the effectiveness of strategies to improve healthcare provider (HCP) performance in low-income and middle-income countries. The review identified strategies with varying effects, including in-service training, supervision and group problem-solving. However, whether their effectiveness changed over time remained unclear. In particular, understanding whether effects decay over time is crucial to improve sustainability. METHODS: We conducted a secondary analysis of data from the aforementioned review to explore associations between time and effectiveness. We calculated effect sizes (defined as percentage-point (%-point) changes) for HCP practice outcomes (eg, percentage of patients correctly treated) at each follow-up time point after the strategy was implemented. We estimated the association between time and effectiveness using random-intercept linear regression models with time-specific effect sizes clustered within studies and adjusted for baseline performance. RESULTS: The primary analysis included 37 studies, and a sensitivity analysis included 77 additional studies. For training, every additional month of follow-up was associated with a 0.19 %-point decrease in effectiveness (95% CI: -0.36 to -0.03). For training combined with supervision, every additional month was associated with a 0.40 %-point decrease in effectiveness (95% CI: -0.68 to -0.12). Time trend results for supervision were inconclusive. For group problem-solving alone, time was positively associated with effectiveness, with a 0.50 %-point increase in effect per month (95% CI: 0.37 to 0.64). Group problem-solving combined with training was associated with large improvements, and its effect was not associated with time. CONCLUSIONS: Time trends in the effectiveness of different strategies to improve HCP practices vary among strategies. Programmes relying solely on in-service training might need periodical refresher training or, better still, consider combining training with group problem-solving. Although more high-quality research is needed, these results, which are important for decision-makers as they choose which strategies to use, underscore the utility of studies with multiple post-implementation measurements so sustainability of the impact on HCP practices can be assessed.
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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 | 0.048 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.007 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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; both teacher heads agree on what is shown here.
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".