Tolerance of ambiguity is not related to decision-making styles in undergraduate nursing students
Bibliographic record
Abstract
Relationships of tolerance of ambiguity, decision-making style, risk-taking behaviors, and the use of supportive and complex care in end-of-life scenarios was investigated in this descriptive correlational study of 377 undergraduate nursing students. The mean for rational decision-making style was 2.332 (agree), while the overall mean for intuitive decision-making was 2.406 (range = 2.37 to 2.489) among all students although higher among sophomore students (2.489, SD = 0.655). The median tolerance of ambiguity scores was higher for juniors and seniors (9.00) compared to sophomore students (8.00). Intuitive decision-making was not associated with level of education. There was no statistically significant correlation between decision-making style and tolerance of ambiguity although there was a negative correlation between intuitive decision-making and tolerance of ambiguity (rs = -0.031, p = .547). Additionally, there was a negative small correlation between rational decision-making and tolerance of ambiguity (rs = -0.040, p = .441). Finally, there was a small statistically significant correlation for supportive care for vignette 1(rs = 0.119, p = .021). All correlations between intuition decision-making and supportive care were low (rs = –0.067-0.119). In conclusion, decision-making style was not related to supportive care. Although intuitive decision-making style was used more frequently by sophomores, there was no statistically significant difference between level of education and decision-making style or tolerance of ambiguity.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".