Use of WONCA global standards to evaluate family medicine postgraduate education for curriculum development and review in Nepal and Myanmar
Bibliographic record
Abstract
Family medicine is an integral part of primary care within health systems. Globally, training programmes exhibit a great degree of variability in content and skill acquisition. While this may in part reflect the needs of a given setting, there exists standard criteria that all family medicine programmes should consider core activities. WONCA has provided an open-access list of standards that their expert community considers essential for family medicine (GP) post-graduate training. Evaluation of developing or existing training programmes using these standards can provide insight into the degree of variability, gaps within programmes and equally as important, gaps within recommendations. In collaboration with the host institution, two family medicine programmes in Nepal and Myanmar were evaluated based on WONCA global standards. The results of the evaluation demonstrated that such a process can allow for critical review of curriculum in various stages of development and evaluation. The implications of reviewing training programmes according to WONCA standards can lead to enhanced training world-wide and standardisation of training for post-graduate family medicine.
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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 | 0.372 | 0.515 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.053 | 0.033 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".