Having our cake and eating it too: seeking the best of both worlds in expertise research
Bibliographic record
Abstract
CONTEXT: Education researchers in a variety of disciplines have attempted to use their understanding of expert processes to inform learning across the continuum from school learning to lifelong learning. In medical education, this has led to models of expertise that aim to understand accurate and efficient clinical reasoning. More recently, researchers outside medicine have begun to develop models of 'adaptive expertise'. As these additional constructions of expertise are introduced into health professions education, there is considerable potential to enhance research in medical expertise by providing opportunities for us to identify our implicit assumptions and reflect on the ways in which our theoretical lenses bias our perceptions of what it means to be an expert. METHODS: Firstly, we critically examine these two broad categories of research on expertise and their underlying assumptions and implications. Our exploration is organised around four main questions: (i) How is expertise defined? (ii) How does it develop? (iii) What is investigated? (iv) Based on what is known, what does an expert look like? Secondly, we discuss some implications and topics of future inquiry for research programmes informed by an inclusive understanding of expert practice and development. CONCLUSIONS: In articulating two paradigms of expertise, our goal is to explore the research questions, methods and findings that underpin them and to make explicit the resulting emphases on specific aspects of expert performance. Our resulting collaborative understanding of expertise yields a richer, more complex and ultimately more accurate view of expert performance, with important implications for future research in medical education.
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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 | 0.099 | 0.117 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.016 | 0.098 |
| Scholarly communication | 0.029 | 0.051 |
| Open science | 0.003 | 0.023 |
| Research integrity | 0.011 | 0.015 |
| Insufficient payload (model declined to judge) | 0.005 | 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".