A repertoire of leadership attributes: An international study of deans of nursing
Bibliographic record
Abstract
Aim: To determine which characteristics of academic leadership are perceived to be necessary for nursing deans to be successful. Background Effective leadership is essential for the continued growth of the discipline. Method: A qualitative study using semi-structured interviews with 30 deans (academics in universities who headed a nursing faculty and degree programmes) was conducted in three countries - Canada, England and Australia. The conversations were analysed for leadership attributes. Result: Sixty personal and positional attributes were nominated by the participants. Of these, the most frequent attribute was 'having vision'. Personal attributes included: passion, patience, courage, facilitating, sharing and being supportive. Positional attributes included: communication, faculty development, role modelling, good management and promoting nursing. Conclusion: Both positional and personal aspects of academic leadership are important to assist in developing a succession plan and education for new deans. Implications for nursing management: It is important that talented people are recognised as potential leaders of the future. These future leaders should be given every chance to grow and develop through exposure to opportunities to develop skills and the attributes necessary for effective deanship. Strategic mentoring could prove to be useful in developing and supporting the growth of future deans of nursing.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| 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".