What healthcare students do with what they don't know: The socializing power of ‘uncertainty’ in the case presentation
Bibliographic record
Abstract
Healthcare students learn to manage clinical uncertainty amid the tensions that emerge between clinical omniscience and the ‘truth for now’ realities of the knowledge explosion in healthcare. The case presentation provides a portal to viewing the practitioner's ability to manage uncertainty. We examined the communicative features of uncertainty in 31 novice optometry case presentations and considered how these features contributed to the development of professional identity in optometry students. We also reflected on how these features compared with our earlier study of medical students' case presentations. Optometry students, like their counterparts in medicine, displayed a novice rhetoric of uncertainty that focused on personal deficits in knowledge. While optometry and medical students shared aspects of this rhetoric ( seeking guidance and deflecting criticism), optometry students displayed instances of owning limits while medical students displayed instances of proving competence. We found that the nature of this novice rhetoric was shaped by professional identity (a tendency to assume an attitude of moral authority or defer to a higher authority) and the clinical setting (inpatient versus outpatient settings). More explicit discussions regarding uncertainty may help the novice unlock the code of contextual forces that cue the savvy member of the community to sanctioned discursive strategies.
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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 | 0.024 | 0.107 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.014 | 0.024 |
| Scholarly communication | 0.016 | 0.015 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.003 | 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".