Men in nursing: history, challenges, and opportunities
Bibliographic record
Abstract
* About the Editors * Preface * Contributors * Foreword, Eleanor J. Sullivan, PhD, RN, FAAN Part I. Our History, Chad E. O'Lynn * History of Men in Nursing: A Review, Chad E. O'Lynn * American Schools of Nursing for Men, Russell E. Tranbarger * The American Assembly for Men in Nursing (AAMN): The First 30 Years as Reported in Interaction, Russell E. Tranbarger * Army Nursing: A Personal Biography, William Bester Part II. Current Issues, Chad E. O'Lynn * The Effects of Gender on Communication and Workplace Relations, Christina G. Yoshimura and Sara Hayden * Men, Caring, and Touch, Chad E. O'Lynn * Reverse Discrimination in Nursing Leadership: Hitting the Concrete Ceiling, Tim Porter-O'Grady * Leadership: How to Achieve Success in Nursing Organizations, Daniel J. Pesut * Gender-Based Barriers for Male Students in Nursing Education Programs, Chad E. O'Lynn Part III. International Perspectives, Chad E. O'Lynn * Gender-Based Barriers for Male Students in General Nursing Education Programs: An Irish Perspective, Brian J. Keogh and Chad E. O'Lynn * Men in Nursing in Canada: Past, Present, and Future Perspectives, Wally J. Bartfay * Men in Nursing: An International Perspective, Larry Purnell Part IV. Future Directions, Russell E. Tranbarger * Recruitment and Retention of Men in Nursing, Susan A. LaRocco * Are You Man Enough to be a Nurse? Challenging Male Nurse Media Portrayals and Stereotypes, Deborah A. Burton and Terry R. Misener * Men's Health: A Leadership Role for Men in Nursing, Demetrius J. Porche * Epilogue, Russell E. Tranbarger * Index.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".