Using eye-tracking augmented cognitive task analysis to explore healthcare professionals' cognition during neonatal resuscitation
Bibliographic record
Abstract
AIM: We aimed to describe the cognitive processes of healthcare providers participating as airway leads in delivery room neonatal resuscitations using eye-tracking assisted debriefing to facilitate recall and provide situational context. METHODS: Delivery room neonatal resuscitations were recorded using eye-tracking glasses worn by participants who acted as airway leads. These glasses analyze eye-movements to produce an audio-visual recording approximating what was "seen" by the participant and marking their visual attention. Participants then reviewed and debriefed their recordings. Debriefing involved a retrospective think-aloud prompted by eye-tracked recordings and an integrated semi-structured interview. Debriefing sessions were transcribed and subjected to thematic analysis. RESULTS: Eight healthcare providers participated in 10 interviews; two providers participated twice in two separate resuscitations. Most visual attention was directed at the infant (62%), with 16% directed to monitors/gauges, 3% to team members. Five major themes emerged including situation awareness, performance, working in teams, addressing threats to performance, and perception of eye-tracking. Information processing was complex and involved top-down and bottom-up processing of environmental stimuli, integration of knowledge/experience, and anticipation of patient response. Despite the focus on individual cognition, interpersonal interactions and teamwork emerged as key aspects of resuscitation performance. Potential threats to performance include equipment issues, mental stress, distractions, and parental presence. Eye-tracking recordings were well-received by the participants. CONCLUSION: Retrospective think-aloud prompted by point-of-view eye-tracked recordings is a useful means of examining cognition of healthcare providers during neonatal resuscitation. Themes identified in this project aligned with existing models of clinical reasoning.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".