Balancing closure and discovery: Adaptive expertise in the workplace
Bibliographic record
Abstract
Abstract BackgroundResidents must develop the knowledge and skills to handle an everchanging and demanding clinical workplace which requires a high degree of adaptability. To address this need, adaptive expertise has been suggested as an important framework for health professions education. However, research on the development of adaptive expertise has yet to explore how workplace supervision impacts residents’development. This study sought to investigate how clinical supervision might support the development of adaptive expertise.MethodsThe present study used a focused ethnography in two emergency departments. We observed 75 supervising situations with the 27 residents resulting in 116 pages of field notes. The majority of supervision was provided by senior physicians, but also included other healthcare professionals.ResultsWe found that supervision could serve two purposes: closure or discovery. Supervision aimed at discovery included practices that reflected instructional approaches said to promote adaptive expertise, such as productive struggle. Supervison aimed at closure included practices which reflected instructional approaches said to be important for efficient and safe patient care, such as verifying information. Our results suggest that supervision is a shared practice and responsibility.ConclusionWe argue that setting and aligning expectations before engaging in supervision is important. Furthermore, results demonstrated that supervision aimed towards discovery was not significantly more time consuming, and a feasible mode of supervision in appropriate situations.
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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 | 0.007 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".