Remodelling general practice training: Tension and innovation
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: The transfer of general practice training in Australia to the two general practice colleges is an opportunity for change in the model of training. The dialectical theory of institutional change suggests that change occurs where organisational structures of training are in tension with the needs of those delivering training, and effective change arises from innovation within these tension points. These tensions have also been faced by general practice training organisations internationally, where solutions have also been crafted. By exploring training tensions and responses to these, the aim of this study was to inform the remodelling of general practice training in Australia. METHOD: Senior educators and stakeholder representatives in Australia and internationally were interviewed to identify tensions in training delivery and innovative responses to these. An interpretative qualitative analysis was undertaken. RESULTS: Eight key tensions and associated innovative responses were identified. DISCUSSION: Drawing from the findings, this article provides recommendations for remodelling general practice training in Australia.
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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 | 0.046 | 0.058 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.015 | 0.047 |
| Scholarly communication | 0.016 | 0.013 |
| Open science | 0.003 | 0.020 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 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".