Advances in clinical education: a model for infectious disease training for mid-level practitioners in Uganda
Bibliographic record
Abstract
Advances in health professional education have been slow to materialize in many developing countries over the past half-century, contributing to a widening gap in quality of care compared to developed countries. Recent calls for reform in global health professional education have stressed, among other priorities, the need for approaches that strengthen clinical reasoning skills. While the development of these skills is critical to enhance health systems, little research has been carried out on the effectiveness of applying these strategies in the context of severe human resource shortages and complex disease presentations. Integrated Infectious Disease Capacity Building Evaluation (IDCAP) based at the Infectious Diseases Institute at Makerere University created a training program using current best practices in clinical education to support the development of complex reasoning skills among clinicians in rural Uganda. Over a period of 9 months, the program integrated classroom and clinic-based training approaches and measured indicators of success with particular reference to common infectious diseases. This article describes in detail the IDCAP approach to integrating advances in health professional education theory in the context of an overburdened, inadequately resourced primary health care system; results from the evaluation are expected in 2012.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".