How student models of expertise and innovation impact the development of adaptive expertise in medicine
Bibliographic record
Abstract
OBJECTIVES: The ability to innovate new solutions in response to daily workplace challenges is an important component of adaptive expertise. Exploring how to optimally develop this skill is therefore of paramount importance to education researchers. This is certainly no less true in health care, where optimal patient care is contingent on the continuous efforts of doctors and other health care workers to provide the best care to their patients through the development and incorporation of new knowledge. Medical education programmes must therefore foster the skills and attitudes necessary to engage future doctors in the systematic development of innovative problem solving. The aim of this paper is to describe the perceptions and experiences of medical students in their third and fourth years of training, and to explore their understanding of their development as adaptive experts. METHODS: A sample of 25 medical students participated in individual 45-60-minute semi-structured interviews. Interviews were audiotaped, transcribed and entered into NVivo qualitative data analysis software to facilitate a thematic analysis. The analysis was both inductive, in that themes were generated from the data, and deductive, in that our data were meaningful when interpreted in the context of theories of adaptive expertise. RESULTS: Participants expressed a general belief that, as learners in the health care system, exerting any effort to be innovative was beyond the scope of their responsibilities. Generally, students suggested that innovative practice was the prerogative of experts and an outcome of expert development centred on the acquisition of knowledge and experience. CONCLUSIONS: Students' perceptions of themselves as having no responsibility to be innovative in their learning process have implications for their learning trajectories as adaptive experts.
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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 | 0.009 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".