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Record W2859340020 · doi:10.1097/nne.0000000000000560

Gaze Performance Adjustment During Needlestick Application

2018· article· en· W2859340020 on OpenAlexaff
Yerly Paola Sanchez, Barbara Wilson-Keates, Adam Conway, Bin Zheng

Bibliographic record

VenueNurse Educator · 2018
Typearticle
Languageen
FieldMedicine
TopicInfection Control in Healthcare
Canadian institutionsAlberta Environment and Protected Areas
Fundersnot available
KeywordsGazeTask (project management)Eye trackingSyringeEye movementTracking (education)PsychologyMedicineComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

BACKGROUND: Novice health care students suffer more needlestick injuries (NSIs) than experts. NSIs may be prevented by learning experts' behavior during this procedure. Eye tracking offers the possibility to study both experts' and novices' eye behavior during this task. PURPOSE: The aim of this study was to offer novel information about the understanding of eye behavior in human errors during handling needles. METHODS: A group of third-year nursing students performed 3 subcutaneous injections in a simulated abdominal pad while their eye behavior was recorded. Similarly, the gaze patterns of experts were recorded and then compared with the novices. RESULTS: Total task time for experts was faster than that for novices (P < .001), but both groups showed similar accuracy (P = .959). However, novices demonstrated gazing longer at the syringe rather than the abdominal pad compared with experts (P = .009). Finally, experts demonstrated fewer attention switches than novices (P = .002). CONCLUSION: Novices demonstrated more tool-tracking eye behaviors with longer dwelling time and attentional switches than did experts, which may translate into errors in clinical performance with needles.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.226
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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.

Opus teacher head0.011
GPT teacher head0.312
Teacher spread0.301 · 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 teacher head, not a consensus.

Study designObservational
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

Citations7
Published2018
Admission routes1
Has abstractyes

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