Exploring facilitator gaze patterns during difficult debriefing through eye-tracking analysis: a pilot study
Bibliographic record
Abstract
Background Managing difficult debriefing can be challenging for simulation facilitators. Debriefers may use eye contact as a strategy to build and maintain psychological safety during debriefing. Visual dominance ratio (VDR), a measure of social power, is defined as the percentage of time making eye contact while speaking divided by the percentage of time making eye contact while listening. Little is known about eye gaze patterns during difficult debriefings. Aim To demonstrate the feasibility of examining eye gaze patterns (i.e. VDR) among junior and senior facilitators during difficult debriefing. Methods We recruited 10 trained simulation facilitators (four seniors and six juniors) and observed them debriefing two actors. The actors were scripted to play the role of learners who were engaged in the first scenario, followed by upset (emotional) and confrontational in the second and third scenarios, respectively. The participant facilitators wore an eye-tracking device to record their eye movements and fixation duration. The fixation durations and VDRs were calculated and summarized with median and interquartile range. We explore the effect of scenarios and training level on VDRs using Friedman tests and Wilcoxon rank sum tests. Results All 10 participants completed all three scenarios. There were no statistically significant differences in VDRs between the junior and senior facilitators for all three scenarios (baseline: p = 0.17; confrontational: p = 0.76; and emotional: p = 0.61). The VDR did not change significantly between scenarios among junior (p = 0.85) and senior facilitators (p = 0.78). The senior group showed higher variability in VDR than the junior group. Conclusion The use of eye-tracking device to measure VDR during debriefings is feasible. We did not demonstrate a difference between junior and seniors in eye gaze patterns during difficult debriefings.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".