Trainee Uncertainty around Intervening When Patients Decompensate
Bibliographic record
Abstract
Abstract Background Trainees in acute care specialties often grapple with the decision to perform an invasive procedure in a rapidly decompensating patient, for whom the benefits and risks are inherently uncertain. The difference between trainees who know when to act and when to seek supervision and those who do not is often linked to individual trainee psychological and cultural perceptions of uncertainty. But how much comfort with uncertainty relates to the situational context rather than the trainee traits is underexplored. Objective The objective of this study was to explore trainee actions around decompensating patients and assess the degree to which invasive intervention and supervision seeking depend on situational certainty or individual trait-based perceptions of uncertainty. Methods A total of 41 internal medicine residents completed a survey to measure anxiety related to uncertainty using the Physicians’ Reactions to Uncertainty (PRU) tool and to measure uncertainty avoidance using the Values Survey Module (VSM) before responding to 14 written emergency situations. Half of the scenarios contain sufficient diagnostic certainty to warrant aggressive intervention, and half lack sufficient diagnostic clarity to offset the risk of intervention. Mixed multivariable modeling was used to identify the relationship between planned invasive intervention, situational uncertainty, and trait-based perceptions of uncertainty measured in the PRU and VSM. Results Trainees’ first actions were appropriate in 60% of cases. Multivariable modeling suggested that situational certainty was more predictive of upfront intervention (odds ratio [OR], 30.5; P < 0.0001) than trait-based PRU (OR, 1.22; P = 0.05) and VSM (OR, 1.73; P < 0.0001). Similarly, situational certainty was more predictive of reduced supervision seeking (OR, 0.20; P < 0.0001) than trait-based PRU (OR, 2.03; P < 0.001) and VSM (P = not significant). Conclusion:s Situation-specific certainty was more strongly correlated with invasive intervention in cases of decompensated patients than individual trainee traits. Focusing on trainee contextual understanding of procedural risk–benefit ratios in decompensating patients holds more promise for improving trainee actions and supervision seeking than tackling their perceptions around uncertainty.
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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 | 0.002 | 0.019 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".