Skeptical self‐regulation: Resident experiences of uncertainty about uncertainty
Bibliographic record
Abstract
OBJECTIVES: Managing uncertainty is central to expert practice, yet how novice trainees navigate these moments is likely different than what has been described by experienced clinicians. Exploring trainees' experiences with uncertainty could therefore help explicate the unique cues that they attend to, how they appraise their comfort in these moments and how they enact responses within the affordances of their training environment. METHODS: Informed by constructivist grounded theory, we explored how novice emergency medicine trainees experienced and managed clinical uncertainty in practice. We used a critical incident technique to prompt participants to reflect on experiences with uncertainty immediately following a clinical shift, exploring the cues they attended to and the approaches they used to navigate these moments. Two investigators coded line-by-line using constant comparison, organising the data into focused codes. The research team discussed the relationships between these codes and developed a set of themes that supported our efforts to theorise about the phenomenon. RESULTS: We enrolled 13 trainees in their first two years of postgraduate training across two institutions. They expressed uncertainty about the root causes of the patient problems they were facing and the potential management steps to take, but also expressed a pervasive sense of uncertainty about their own abilities and their appraisals of the situation. This, in turn, led to challenges with selecting, interpreting and using the cues in their environment effectively. Participants invoked several approaches to combat this sense of uncertainty about themselves, rehearsing steps before a clinical encounter, checking their interpretations with others and implicitly calibrating their appraisals to those of more experienced team members. CONCLUSIONS: Trainees' struggles with the legitimacy of their interpretations impact their experiences with uncertainty. Recognising these ongoing struggles may enable supervisors and other team members to provide more effective scaffolding, validation and calibration of clinical judgments and patient management.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.090 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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 teacher head, 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".