An Exploratory Study of Clinical Decision‐Making in Five Countries
Bibliographic record
Abstract
PURPOSE: To identify the cognitive processes nurses use in their decision-making in long- and short-term care settings in five countries, and the demographic variables associated with their decision-making. METHOD AND SAMPLES: The instrument used was a 56-item questionnaire that has been shown to be reliable in earlier studies. The sample consisted of five convenience samples of registered nurses working in either geriatric wards (n = 236) or acute medical-surgical wards (n = 223) in hospitals or nursing homes in Canada, Finland, Sweden, Switzerland, and the United States. FINDINGS: Five models of decision-making were identified on the basis of factor analysis. They represent both analytical and intuitive cognitive processes. Analytical cognitive processes were emphasized in information collection, problem definition, and planning of care, and intuitive cognitive processes were emphasized in planning, implementing, and evaluating care. Professional education, practical experience, field of practice, and type of knowledge were significantly associated with decision-making models as well as with country of residence of the participants. The highest proportion of analytically oriented decision-makers was found among nurses in long-term care, the decision-making of nurses in short-term care was more intuitively oriented. CONCLUSIONS: The results indicate that decision-making of participants varied from country to country and in different nursing situations. Future research should be focused on reasons for these differences, the relationship between the task and the nurses' type of knowledge, and how nurses use their knowledge to make decisions in different nursing situations.
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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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".