Clinical judgement within the South African clinical nursing environment: A concept analysis
Bibliographic record
Abstract
Reform in the South African healthcare and educational system were characterized by the ideals that the country needs to produce independent, critical thinkers. Nurses need to cope with diversity in a more creative way, defining their role in a complex, uncertain, rapidly changing health care environment. Quality clinical judgement is therefore imperative as an identified characteristic of newly qualified professional nurses. The objective of this study was to explore and describe clinical judgement through various data sources and review of literature to clarify the meaning and promote a common understanding through formulating the characteristics and developing a connotative (theoretical) definition of the concept. An explorative, descriptive qualitative design was used to discover the complexity and meaning of the phenomenon. Multiple data sources and search strategies were used, for the time frame 1982—2013. A concept analysis was used to arrive at a theoretical definition of the concept of ‘clinical judgement’ as a complex cognitive skill to evaluate patient needs, adaption of current treatment protocols as well as new treatment strategies, prevention of adverse side effects through being proactive rather than reactive within the clinical nursing environment. The findings emphasized clinical judgement as skill within the clinical nursing environment, thereby improving autonomous and accountable nursing care. These findings will assist nurse leaders and clinical nurse educators in developing a teaching-learning strategy to promote clinical judgement in undergraduate nursing students, thereby contributing to the quality of nursing care.
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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 | 0.014 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.005 | 0.013 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.005 |
| 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".