Eye-Tracking for Examining Nurses’ Attention During Cardiac Arrest Simulations: A Feasibility and Acceptability Study
Bibliographic record
Abstract
Introduction: Eye-tracking offers a distinctive opportunity to assess nurses’ clinical decision-making in simulation. Although its feasibility has been established in various scenarios, most studies have focused on a single participant, typically a physician in a leadership role. The application of eye-tracking in the challenging context of in-hospital cardiac arrest (IHCA) simulations, where nurses juggle diverse roles and undertake physical tasks such as chest compressions, has yet to be explored. Objectives: This study aimed to assess the feasibility and acceptability of eye-tracking with nurses’ during IHCA simulations. Additionally, the study aimed to describe eye-tracking metrics based on different resuscitation roles and to explore the relationship between eye-tracking metrics to pinpoint the most informative metrics for the design of future studies. Methods: In this single-group observational study, 56 newly hired nurses wore eye-tracking glasses during IHCA simulations. The primary feasibility criterion was the proportion of usable eye-tracking data. Secondary criteria included recruitment rate, calibration time, and glasses acceptability. The relationship among eye-tracking metrics was investigated through correlation analyses. Results: Calibration of the devices was rapid, and 85.7% of the data was usable. The glasses were comfortable, non-distracting, and did not impede nurses’ vision or performance. Data were mapped for five areas of interest: the patient’s head and chest, cardiac monitor, teammates, and resuscitation cart. Eye-tracking metrics exhibited variations based on resuscitation roles. Fixation count, fixation duration, and time to first fixation appeared to be the most informative metrics in IHCA simulation. Discussion and conclusion: These findings demonstrate the feasibility and acceptability of analyzing nurses’ eye-tracking data during IHCA simulations using a role-based approach. Future research should explore correlations with additional attention measures to enhance our understanding of nurse decision-making during cardiac arrest and improve educational strategies and outcomes.
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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 | 0.021 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".