Peer training and co‐learning in global health care
Bibliographic record
Abstract
BACKGROUND: The Zambia Anaesthesia Development Project (ZADP) is a an international health care partnership between the UK and Zambia, designed to mutually benefit anaesthetic registrars from both countries in aspects deficient in their native training schemes. CONTEXT: The ZADP evolved from an initial plan to support a new anaesthesia training programme in Zambia, with visiting trainees providing continuity that was missing from short-term consultant visits. The primary goal of the project was to improve patient safety and care by delivering teaching, and by developing management, leadership and governance structures. An important reciprocal goal was to provide a unique training experience for a UK registrar in a resource-poor environment, including the resulting clinical and non-clinical challenges. This model adds a useful dimension to the educational support provided, and also embodies the principle of co-development IMPLICATIONS: The ZADP and the Zambia Master of Medicine (MMed) Anaesthesia programme provides an example of a cross-cultural peer-directed co-learning model that benefits trainees from developed and developing postgraduate training programmes. This synergistic model is one that could be applied to other educational initiatives supported from overseas. This model not only adds a useful dimension to the educational support provided, but also embodies the principle of co-development that is so important to the sustainability of such projects.
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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 | 0.025 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.022 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.002 | 0.021 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".