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Record W4327614975 · doi:10.3389/fped.2023.1116893

Use of eye-tracking to evaluate human factors in accessing neonatal resuscitation equipment and medications for advanced resuscitation: A simulation study

2023· article· en· W4327614975 on OpenAlexafffund
Linda Gai Rui Chen, Brenda Hiu Yan Law

Bibliographic record

VenueFrontiers in Pediatrics · 2023
Typearticle
Languageen
FieldMedicine
TopicSimulation-Based Education in Healthcare
Canadian institutionsUniversity of Alberta
FundersAlberta Innovates
KeywordsMedicineResuscitationNeonatal resuscitationMedical emergencyEmergency medicineIntensive care medicine

Abstract

fetched live from OpenAlex

Introduction Emergency neonatal resuscitation equipment is often organized into “code carts”. Simulation studies previously examined human factors of neonatal code carts and equipment; however, visual attention analysis with eye-tracking might further inform equipment design. Objectives To evaluate human factors of neonatal resuscitation equipment by: (1) comparing epinephrine preparation speed from adult pre-filled syringe vs. medication vial, (2) comparing equipment retrieval times from two carts and (3) utilizing eye-tracking to study visual attention and user experience. Methods We conducted a 2-site randomized cross-over simulation study. Site 1 is a perinatal NICU with carts focused on airway management. Site 2 is a surgical NICU with carts improved with compartments and task-based kits. Participants were fitted with eye-tracking glasses then randomized to prepare two epinephrine doses using two methods, starting with an adult epinephrine prefilled syringe or a multiple access vial. Participants then obtained items for 7 tasks from their local cart. Post-simulation, participants completed surveys and semi-structured interviews while viewing eye-tracked video of their performance. Epinephrine preparation times were compared between the two methods. Equipment retrieval times and survey responses were compared between sites. Eye-tracking was analyzed for areas of interest (AOIs) and gaze shifts between AOIs. Interviews were subject to thematic analysis. Results Forty HCPs participated (20/site). It was faster to draw the first epinephrine dose using the medication vial (29.9s vs. 47.6s, p < 0.001). Time to draw the second dose was similar (21.2s vs. 19s, p = 0.563). It was faster to obtain equipment from the Perinatal cart (164.4s v 228.9s, p < 0.027). Participants at both sites found their carts easy to use. Participants looked at many AOIs (54 for Perinatal vs. 76 for Surgical carts, p < 0.001) with 1 gaze shifts/second for both. Themes for epinephrine preparation include: Facilitators and Threats to Performance, and Discrepancies due to Stimulation Conditions. Themes for code carts include: Facilitators and Threats to Performance, Orienting with Prescan, and Suggestions for Improvement. Suggested cart improvements include: adding prompts, task-based grouping, and positioning small equipment more visibly. Task-based kits were welcomed, but more orientation is needed. Conclusions Eye-tracked simulations provided human factors assessment of emergency neonatal code carts and epinephrine preparation.

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 imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.013
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.111
GPT teacher head0.454
Teacher spread0.343 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2023
Admission routes2
Has abstractyes

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