Training Australian Defence Force Medical Officers to civilian general practice training standards — reflections on military medicine and its links to general practice education and training
Bibliographic record
Abstract
This article examines military medicine and its links to civilian general practice education and training, drawing attention to the variations and difficulties in, and successful approaches for, training Australian Defence Force (ADF) Medical Officers. Military medicine has been an area of change over the 10 years of the Australian General Practice Training (AGPT) program. Crisis situations like those in Timor Leste and Afghanistan have focused attention and recognition on the importance of primary health care in the work of the ADF. To train doctors in military medicine, there are several different models at different locations around Australia, as well as large variations in military course and experience recognition and approvals between AGPT regional training providers. At times, the lack of standardisation in training delays the progress of ADF registrars moving through the AGPT program and becoming independently deployable Medical Officers.
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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 | 0.019 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".