Intuition in Clinical Decision Making: Differences Among Practicing Nurses
Bibliographic record
Abstract
PURPOSE: To examine the relationships and differences in the use of intuition among three categories of practicing nurses from various clinical units at a medical center in the Midwest. DESIGN: Descriptive, correlational, cross-sectional, prospective design. METHOD: Three categories of nurses were based on the clinical unit: medical/surgical nurses ( n = 42), step-down/progressive care nurses ( n = 32), and critical care nurses ( n = 24). Participants were e-mailed the Rew Intuitive Judgment Scale (RIJS) via their employee e-mail to measure intuition in clinical practice. Participants were also asked to rate themselves according to Benner's (novice to expert) proficiency levels. FINDINGS: Nurses practicing at higher self-reported proficiency levels, as defined by Benner, scored higher on the RIJS. More years of clinical experience were associated with higher self-reported levels of nursing proficiency and higher scores on the RIJS. There were no differences in intuition scores among the three categories of nurses. CONCLUSION: Nurses have many options, such as the nursing process, evidence-based clinical decision-making pathways, protocols, and intuition to aid them in the clinical decision-making process. Nurse educators and development professionals have a responsibility to recognize and embrace the multiple thought processes used by the nurse to better the nursing profession and positively affect patient outcomes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.218 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".