The transition experiences of novice mid-level academic nurse leaders from educators to administrators: A qualitative study
Bibliographic record
Abstract
Objective: The shortage of academic nurse leaders (ANLs) is imminent and creates a crisis for the nursing profession. Academic administrators influence the nursing workforce’s preparation. When nursing programs and educators fail to address the urgent nursing shortage, this can result in short- and long-term impacts for the nursing profession. The purpose of this qualitative study was to explore the experiences of novice mid-level ANLs who transitioned from educators to academic administrators.Methods: A basic qualitative research design was conducted to gain insight into the transition experiences of 10 novice mid-level ANLs. A purposeful sampling technique was used to recruit and select qualified study participants. Individual interviews via Zoom were used to collect data from participants. The researcher used a semi-structured, open-ended interview protocol. A thematic analysis with a constant comparison method, was used to analyze the data.Results: The interview results yielded six themes, which included (1) transitioning into an academic nurse leader, (2) role preparation and professional development, (3) having support, (4) insufficient time, (5) public challenges, and (6) feeling confident but still learning.Conclusions: The study results revealed that role preparation, professional development, and support were essential facilitators during the transition process. The results also suggested that early role preparation of emerging ANLs could build a cadre of qualified, well-prepared academic administrators, thus ensuring academic leadership succession. Recommendations for practice included role socialization, individualized leadership development programs, and formal structured mentoring.
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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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".