Effective Strategies for Global Health Training Programs A Systematic Review of Training Outcomes in Low and Middle Income Countries
Bibliographic record
Abstract
<p><strong>INTRODUCTION: </strong>Low and middle-income countries face a continued burden of chronic illness and non-communicable diseases while continuing to show very low health worker utilization. With limited numbers of medical schools and a workforce shortage the poor health outcomes seen in many low and middle income countries are compounded by a lack of within country medical training.</p><p><strong>METHODS: </strong>Using a systematic approach, this paper reviews the existing literature on training outcomes in low and middle-income countries in order to identify effective strategies for implementation in the developing world. This review examined training provided by high-income countries to low- and middle-income countries.</p><p><strong>RESULTS: </strong>Based on article eligibility, 24 articles were found to meet criteria. Training methods found include workshops, e-learning modules, hands-on skills training, group discussion, video sessions, and role-plays. Of the studies with statistically significant results training times varied from one day to three years. Studies using both face-to-face and video found statistically significant results.</p><p><strong>DISCUSSION:</strong> Based on the results of this review, health professionals from high-income countries should be encouraged to travel to low- middle-income countries to assist with providing training to health providers in those countries.</p>
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.009 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| 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".