The Effects of Time Pressure and Experience on Nurses' Risk Assessment Decisions
Bibliographic record
Abstract
BACKGROUND: Time pressure and, occasionally, suboptimal assessment decisions are features of nursing in acute care. OBJECTIVES: To explore the effect of generic and specialist clinical experience on the ability to detect the need to take action in acute care and the impact of time pressure on nurses' decision-making performance. METHODS: Experienced acute care registered nurses (n = 241) were presented with 50 vignettes of real clinical risk assessments. Each vignette contained seven information cues. In response to these vignettes, nurses had to decide whether to intervene or not. The 26 vignettes were time limited and mixed randomly into the 50 cases. Signal detection analysis was used to establish nurses' performance, personal decision thresholds ([beta]), and their abilities (d') to distinguish a signal of clinical risk from the clinical noise of noncontributory information. RESULTS: Nurses had significantly lower d' and were significantly less likely to indicate intervening under time pressure. For ability-but not threshold-there was a significant interaction of time pressure and years of experience in acute care. With no time pressure, d' increased in line with years of experience. Under time pressure, there was no effect. DISCUSSION: Time pressure reduced nurses' ability to detect the need and the tendency to report intervening. Thus, there were more failures to report appropriate intervention under time pressure, and the positive effects of clinical experience were negated under time pressure. More and larger scale research on the effect on clinical outcomes of time pressured nursing choices is required.
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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 | 0.007 | 0.100 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".