How do we educate the next generation of emergency physicians: RCEM 50
Bibliographic record
Abstract
> ‘The great aim of education is not knowledge but action.’ H. Spencer The training needs of the next generation are based on the needs of the population they serve.1 These are the basis for curricula and each developed Emergency Medicine (EM) system has one. There is a move away from training based on time served or a list of EM presentations to be covered to one based on generic capabilities and defined activities that an EM clinician will have to do at work. This is happening in Australasian, Canadian and now UK EM education. Generic competences for EM training has been described by FACEM in Australia in a curriculum framework.2 The Canadian Association of Emergency Physicians are changing their curriculum design as part of a national move to Competency-Based Medical Education (CBME).3 They have had a national generic framework of competence, Can MEDS, against which doctors in postgraduate training have been evaluated since 2005.4 In their new CBME programme, doctors will be adjudged competent in key activities of practice, in which they will demonstrate they meet Can MEDS competencies, before they can move on to the next stage of training. These activities are those that an EM clinician will have to be able deliver independently to complete training. Such activities have been described as Entrustable Professional Activities (EPAs)—‘Professional activities that together constitute the mass of critical elements that operationally define a profession’.5 In 2017, the UK General Medical Council (GMC), in its guidance for curriculum design, has incorporated the need for all UK medical training programmes to include Generic Professional Capabilities (GPCs) and that these are introduced across all specialties by 2020.6 The GMC state: > ‘The primary purpose of GPCs is to describe the fundamental, career-long, generic capabilities required to develop and maintain key …
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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 | 0.013 | 0.088 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.009 | 0.016 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.024 | 0.027 |
| Insufficient payload (model declined to judge) | 0.070 | 0.042 |
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".