Power and conflict: the effect of a superior's interpersonal behaviour on trainees’ ability to challenge authority during a simulated airway emergency
Bibliographic record
Abstract
A key factor that may contribute to communication failures is status asymmetry between team members. We examined the effect of a consultant anaesthetist's interpersonal behaviour on trainees' ability to effectively challenge clearly incorrect clinical decisions. Thirty-four trainees were recruited to participate in a video-recorded scenario of an airway crisis. They were randomised to a group in which a confederate consultant anaesthetist's interpersonal behaviour was scripted to recreate either a strict/exclusive or an open/inclusive communication dynamic. The scenario allowed trainees four opportunities to challenge clearly wrong decisions. Performances were scored using the modified Advocacy-Inquiry Score. The highest median (IQR [range]) score was 3.0 (2.2-4.0 [1.0-5.0]) in the exclusive communication group, and 3.5 (3.0-4.5 [2.5-6.0]) in the inclusive communication group (p = 0.06). The study did not show a significant effect of consultant behaviour on trainees' ability to challenge their superior. It did demonstrate trainees' inability to challenge their seniors effectively, resulting in critical communication gaps.
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 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 | 0.004 | 0.046 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".