A conceptual framework to facilitate clinical judgement in nursing: A methodological perspective
Bibliographic record
Abstract
The South African health care and education systems are challenged to provide independent, critical thinking nurses who can cope with diversity in a creative way and define their role in a complex, uncertain, rapidly changing health care environment. Quality clinical judgement is an imperative characteristic that newly qualified professional nurses should possess. To accommodate these needs, SANC in line with the SAQA Act, advocated the development of teaching and learning strategies to balance theory and practice opportunities together with an outcome-based, student centred approach and appropriate clinical supervision. This resulted in a positive outcome to facilitate the integration/fusion of theory and practice. The purpose of this study was to synthesise a teaching–learning strategy for creating an enabling learning environment to facilitate clinical judgement in South African undergraduate nursing students. The proposed teaching–learning strategyis grounded in modern-day constructivist approach of learning. The conceptual or theoretical framework of this study aimed to link the central concepts that were identified from the conclusions of four (4) strategic objectives of the two preceding phases of the study into a new structure of meaning that served as a basis for the proposed strategy. The implementation of the proposed action plan to achieve the stated strategic objectives should empower the relevant role players to facilitate clinical judgement in undergraduate nursing students and thereby promote 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 | 0.054 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.006 | 0.046 |
| Scholarly communication | 0.014 | 0.014 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".