Men in nursing: history, challenges, and opportunities
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it