PP504 Topic: AS22–Quality and Safety/Errors/Data Management/Other: PAEDIATRIC CRITICAL CARE FELLOW PERCEPTION OF LEARNING THROUGH VIRTUAL REALITY BRONCHOSCOPY
Bibliographic record
Abstract
Aims & Objectives: Virtual reality (VR) simulators have revolutionized training in bronchoscopy, offering unrestricted availability in a low-stakes learning environment and frequent assessments represented by automatic scoring. The VR assessments can be used to monitor and support learners’ progression. How trainees perceive these assessments needs to be clarified. The objective of this study was to examine what assessments learners select to document and receive feedback on and what influences their decisions. Methods: We used a sequential explanatory mixed methods strategy. All participants were pediatric critical care medicine trainees requiring competency in bronchoscopy skills. During independent simulation practice, we collected the number of learning-focused practice attempts (scores not recorded), assessment-focused practice (scores recorded and reviewed by the instructor for feedback), and the amount of time each attempt lasted. After simulation training, we conducted interviews to explore learners’ perceptions of assessment. Results: There was no significant difference in the number of attempts for each practice type. The average time per learning-focused attempt was almost three times longer than the assessment-focused attempt (mean [standard deviation] 16 ± 1 min vs. 6 ± 3 min, respectively; P < 0.05). Learners perceived documentation of their scores as high stakes and only recorded their better scores. Learners felt safer experimenting if their assessments were not recorded. Conclusions: During independent practice, learners took advantage of automatic assessments generated by the VR simulator to monitor their progression. However, the recording of scores from the simulation program to document learners’ trajectory to a set goal was perceived as high stakes, discouraging learners from seeking supervisor feedback. Keywords: Assessment, bronchoscopy, Learning, Feedback, Simulation
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.102 | 0.008 |
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".