Men in Nursing: A Qualitative Examination of Students’ Stereotypes of Male Nurses through the Framework of Social Role Theory and Stereotype Content Model
Bibliographic record
Abstract
Driven by overwhelming numerical dominance of women in the role of nurses, nursing profession over the last two centuries has been largely scripted with gendered characterizations. However, nuances that shape the language and wording choices that are evoked when describing the stereotypes targeting male nurses remain relatively unexplored. Our current research examined the way 117 female non-nursing and nursing students in Canada characterized male nurses using open-ended self-report measures and thematic qualitative analyses. We contribute to the literature on nursing, gender, and stereotypes by analyzing the personal attitudes and stereotypes held by young female students toward male nurses. Social role theory and the stereotype content model provided the theoretical underpinnings to explore and explain emergent stereotypes and stereotype content. Our findings suggest that students generate more communal, high-warmth characteristics for male nurses than agentic characteristics, suggesting possible paternalistic prejudice toward men in nursing.
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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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| 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".