Professional nurses' understanding of clinical judgement: A contextual inquiry
Bibliographic record
Abstract
Higher cognitive skills are essential competencies for nurses joining the technologically and increasingly complex health care environment to provide safe and effective nursing care. Educators and clinical facilitators have recognised that newly qualified nurses do not meet the expectations for entry level clinical judgement and are held accountable for finding adequate learning experiences as preparation for such practice demands. An explorative and descriptive qualitative design was followed in this study to reach an understanding of clinical judgement in the clinical nursing environment from the perspective of professional nurses. Eleven professional nurses (n = 11) working at primary health care clinics, public and private hospitals participated voluntarily. Data was collected by means of the “World Cafe” method, incorporating a combination of techniques such as interviewing, discussions, drawings, narratives and reflection. The focus was on professional nurses' knowledge of the meaning of clinical judgement and factors influencing the development of clinical judgement in the clinical environment. Qualitative thematic content analysis principles were applied during data analysis. The findings were integrated with the relevant literature to culminate in conclusions that should add to the knowledge base of clinical judgement as an essential skill for improving autonomous and accountable 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 | 0.026 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.025 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.003 |
| 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".