Mapping Leadership in Undergraduate Nursing Regulator Standards and Requirements Across Eleven Countries
Bibliographic record
Abstract
A significant body of evidence from a recent scoping review underscores the critical role of nurse leadership in education, research, and clinical practice, highlighting its direct impact on care quality, patient safety, nursing student education, workforce outcomes, morale, commitment, performance, and retention (Abdul-Rahim et al. 2025). Conversely, poor leadership practices have been linked to adverse patient and organisational outcomes, substandard learning experiences for nursing students, low patient satisfaction, diminished staff morale, and high turnover rates (Abawaji et al. 2024). In response to these challenges, efforts to develop leadership skills in graduate nurses have gained momentum, exemplified by the recent rollout of the International Council of Nurses (ICN) and the World Health Organisation (WHO) leadership programme (ICN 2024). Despite this progress, significant gaps remain in understanding how educational strategies can be effectively integrated into undergraduate nursing curricula to support leadership development. This underscores the urgent need to embed structured leadership education for nursing students, complete with defined competencies for practice, as an essential component of nursing programmes from the first year of study (Baron et al. 2024).
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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 | 0.010 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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".