Exploration of Student Nurses’ Voices Regarding Self-Leadership in Clinical Learning at the Limpopo College of Nursing, South Africa
Bibliographic record
Abstract
BACKGROUND: Self-leadership is explained as a dynamic interaction of cognitive, behavioural and effective elements, geared towards self-influencing actions of an individual within the academic context. The purpose of this study was to determine the views of student nurses regarding self-leadership in clinical learning at the Limpopo College of nursing, South Africa OBJECTIVE: A non- probability, convenience, purposive sampling was used to select 16 students who voluntarily agreed to participate in this study. The researcher conducted semi-structured, one-to-one interviews which were audio recorded and transcribed. Data collection was done and analysed using the Tesch’s inductive, descriptive coding technique. RESULTS: One theme and its sub-themes emerged namely: self-leadership associated with responsibility and accountability, self-leadership viewed as a learned strategy, self-leadership - a difficult act to achieve, self-leadership is a beneficial process during clinical learning periods and self-leadership viewed as the adoption of personal values CONCLUSION: The findings of the study revealed that students shared the same views related to the enhancement of self-leadership in the clinical environment. In this regard, the various strategies were found to be used by students to support self-leadership learning.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".