Bibliographic record
Abstract
BACKGROUND: Nurses work in situations of complex care requiring great clinical reasoning abilities. In literature, clinical reasoning is often confused with other concepts and it has no consensual definition. AIM: To conduct a concept analysis of a nurse's clinical reasoning in order to clarify, define and distinguish it from the other concepts as well as to better understand clinical reasoning. METHOD: Rodgers's method of concept analysis was used, after literature was retrieved with the use of clinical reasoning, concept analysis, nurse, intensive care and decision making as key-words. RESULTS: The use of cognition, cognitive strategies, a systematic approach of analysis and data interpretation, generating hypothesis and alternatives are attributes of clinical reasoning. The antecedents are experience, knowledge, memory, cues, intuition and data collection. The consequences are decision making, action, clues and problem resolution. CONCLUSION: This concept analysis helped to define clinical reasoning, to distinguish it from other concepts used synonymously and to guide future research.
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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 | 0.016 | 0.043 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.017 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
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".