The leadership experience of academic chief nurse administrators post pandemic
Bibliographic record
Abstract
Background and objective: The impact of the COVID-19 global pandemic has been rated as one of the highest factors of nurse leaders to leave the profession, but limited research exists describing academic chief nurse administrators’ (ACNAs) leadership experiences during the pandemic as crisis leadership swept across academia in the United States. The purpose of this qualitative study was to explore the lived experiences of ACNAs in pre-licensure nursing programs in the state of Georgia serving on campus post-pandemic after temporary full virtual instruction and isolation during an ongoing worldwide pandemic.Methods: This Husserlian phenomenological qualitative study combined with Colaizzi’s method of data analysis included a demographic questionnaire and in-depth interviews with seven ACNAs throughout the state of Georgia.Results: Four themes emerged: ACNA Leadership and Challenges, Navigating Leadership Challenges and Obstacles, Managing Support and Work-Life Balance, and Reflection and Moving Forward.Conclusions: This study illuminated ACNAs’ strengths and weaknesses in academic leadership necessitating the need for further discussion, mentorship, development of leadership tools for future crises, and the need for collaboration with clinical nurse leaders.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.008 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.004 |
| 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".