Exploring adaptive expertise in residency: the (missed) opportunity of uncertainty
Bibliographic record
Abstract
Preparing novice physicians for an unknown clinical future in healthcare is challenging. This is especially true for emergency departments (EDs) where the framework of adaptive expertise has gained traction. When medical graduates start residency in the ED, they must be supported in becoming adaptive experts. However, little is known about how residents can be supported in developing this adaptive expertise. This was a cognitive ethnographic study conducted at two Danish EDs. The data comprised 80 h of observations of 27 residents treating 32 geriatric patients. The purpose of this cognitive ethnographic study was to describe contextual factors that mediate how residents engage in adaptive practices when treating geriatric patients in the ED. Results showed that all residents fluidly engaged in both adaptive and routine practices, but they were challenged when engaging in adaptive practices in the face of uncertainty. Uncertainty was often observed when residents' workflows were disrupted. Furthermore, results highlighted how residents construed professional identity and how this affected their ability to shift between routine and adaptive practices. Residents reported that they thought that they were expected to perform on par with their more experienced physician colleagues. This negatively impacted their ability to tolerate uncertainty and hindered the performance of adaptive practices. Thus, aligning clinical uncertainty with the premises of clinical work, is imperative for residents to develop adaptive expertise.
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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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".