Where to now after a learning and educational needs analysis of Fellows of the Australasian College for Emergency Medicine?
Bibliographic record
Abstract
A six-article series by Dent et al. in the February, this and subsequent Emergency Medicine Australasia issues describes results from a global learning and educational needs analysis of Fellows of the Australasian College for Emergency Medicine (FACEM) continuing professional development (CPD).[1][2][3][4][5][6] Only one previous published emergency physician (EP) learning needs analysis was identified that specifically addressed procedures.7 The Australasian College for Emergency Medicine (ACEM), its Maintenance of Professional Standards (MOPS) committee and the authors should be congratulated on initiating and completing such an important undertaking at this critical stage of ACEM development.Part of this undertaking was to progress a professional development course for FACEMs, now known as the Advanced Complex Medical Emergencies (ACME) course, which has run successfully in a number of simulation centres.Recently, the Australian Medical Council (AMC) accredited ACEM for at least 6 years as a professional medical college.ACEM was commended for 'The College's MOPS program, which is well developed, available online and easy to complete, and has a high rate of compliance by fellows with program requirements'.Recommendations by the AMC were that the ACEM: (i) 'Develop an approach to competency testing of procedural skills for fellows, using simulation where possible'; and (ii) 'In considering the issue of retraining, develop a policy that can be used to guide retraining or remediation of fellows, should this need arise'.
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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 | 0.012 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.022 | 0.005 |
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".