Deception in simulation‐based education: a randomised controlled trial of the effect of deliberate deception on the performance of anaesthesia trainees
Bibliographic record
Abstract
The use of deliberate deception in simulation allows for a level of realism that is not normally feasible. However, the use of deception is controversial, and carries the risk of psychological harm to learners. There are currently no quantitative data on the effect of deception on learner performance, making it difficult to judge its usefulness. The objective of this study was to examine the impact of deception on learners' performance during a life-threatening scenario. In this simulation study, second-year anaesthesia residents were randomly allocated into two groups: the non-deception group was told that the participating consultant was acting a part, while the deception group was told that the consultant was a subject in the study. Learners then participated in a simulated crisis that presented them with situational opportunities to challenge the consultant regarding clearly wrong decisions. Two independent raters scored the performances using the modified advocacy-inquiry scale. Forty-four participants were analysed. The median (IQR [range]) highest scoring modified advocacy-inquiry scale was 5.0 (4.5-5.1 [4.0-5.5]) for the non-deception group and 4.0 (3.0-4.0 [2.5-5.0]) for the deception group, (p < 0.001), and the median total number of challenges per participant was 26.8 (21.0-31.1 [16.5-35.5]) and 18.0 (14.3-23.3 [7.0-33.0]), respectively (p = 0.001). Trainees exposed to deliberate deception, who thought that the consultant anaesthetist was a subject, had a less-effective best challenge, likely mimicking real-life behaviour. Deliberate deception appears to modify behaviour, particularly relating to communication involving hierarchical relationships. This technique may improve authenticity, especially with a steep power gradient, and so has demonstrable value which must be balanced against the ethical considerations.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".