Gaze Performance Adjustment During Needlestick Application
Bibliographic record
Abstract
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.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".