Exploring the career pathways of four males nurses to the deanship position in higher education: A narrative inquiry
Bibliographic record
Abstract
Background and purpose: The career path to deanship for male nurses is still mostly unexplored. Male deans leading nursing schools is a new trend in the U.S.Methods: A narrative inquiry using semi-structured interviews with four male deans of schools of nursing in the Southwestern U.S. was the methodology used for this study.Results: The following themes emerged from the data: 1) service to others; 2) traditional career trajectories; 3) it is all about people; and 4) evolving leadership styles. Importance: The participants’ narratives provided first-hand accounts of how these men transitioned from the bedside to the boardroom in higher education. Their experiences could shed light on gender-related issues in nursing education and its leadership. Thus, this study can serve as a career compass for male nurses aspiring to academic leadership positions, inspire more men to join the profession, and aid educational institutions develop strategies for a more gender-balanced workforce.Conclusions: This study proved that men are assets to the nursing profession in both practice and academia. Recruiting more men is part of a solution to the dean and faculty shortage. Preparing the next generation of nursing deans needs a concerted effort to enhance the diversity of the deans and the faculty to reflect the student population today.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.006 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.005 |
| 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".