Bibliographic record
Abstract
BACKGROUND: Research on improving academic leadership in nursing is paramount to increase new faculty support, improve retention, and ensure a high academic standard for the next generation of nurses. However, an operational definition of academic leadership in nursing is missing from the literature and a common language is needed to cohere research. AIM: This study aimed to analyze the concept of academic leadership in nursing to inform future research on the factors that affect nursing faculty career development, job satisfaction, and retention. METHODS: A concept analysis using Walker and Avant's eight-step method. DATA SOURCES: Five databases were searched (CINAHL, PubMed, OVID Emcare, ERIC, and Google Scholar), with 16 articles identified for analysis. The term "academic leadership" was used in a title search with "nursing" as a keyword. RESULTS: Three main attributes were found: administrator, mentor, and nurse. The consequences of academic leadership in nursing are improved work environments, increased job satisfaction, and decreased faculty turnover. Capabilities essential for academic leaders in nursing include vision, risk-taking, excellent communication, mentoring, succession planning, advocacy, and education. CONCLUSION: An academic leader in nursing is a transformational leader who encourages, empowers, and motivates team members to grow, develop and thrive.
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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 | 0.014 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.014 | 0.015 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".