Attitudes and Perceptions towards Men in Nursing Education
Bibliographic record
Abstract
Purpose: The aim of the current investigation was to compare and contrast societal perceptions and attitudes towards men in nursing as reported by male and female nursing and non-nursing university students. Methods: A comparative study design was employed to investigate societal attitudes and perceptions towards male nurses enrolled in school of nursing in a mid-sized university in Ontario, Canada. A convenience non-random sampling method was employed that consisted of undergraduate nursing (N = 82) and non-nursing (N = 67) students. Perceptions and attitudes towards men in nursing were assessed using the Attitudes Towards Men in Nursing Scale (ATMINS). Results: Sixty-seven non-nursing students partook in this investigation, including 36 males and 31 females. The non-nursing female students ranged in age from 19 to 25 years (mean = 21.6 +/- standard deviation [S.D.] 4.9), and non-nursing male students ranged in age from 19 to 37 years (mean = 23.9 +/- 4.9 S.D.). The nursing female students ranged in age from 19 to 44 years (mean = 21.8 +/- 8.8 S.D.), and the male nursing students ranged in age from 19 to 46 years (mean = 28.9 +/- 8.8 S.D.). Overall, both female nursing and non-nursing students were found to have less favourable perceptions and negative attitudes towards male nursing students. Conclusions and Recommendations: Our findings suggest that there is a general perception in Canadian society that nursing is a more suitable career choice for women than men. Moreover, societal perceptions and stereotypes towards male nurses (e.g., they are gay, effeminate, less compassionate and caring than females nurses) prevail, which may negatively contribute to their recruitment and retention in nursing programs. Greater efforts need to be taken to recruit more positive male role models in clinical practice and academia. Furthermore, recruitment ads for nursing should make greater efforts to portray the male presence in the profession.These strategies will help to address in part the current national and global shortages for nurses and also help to encourage diversity in the profession.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".