“Nursing is no place for men” ― A thematic analysis of male nursing students experiences of undergraduate nursing education
Bibliographic record
Abstract
Background: Surprisingly, opinion about whether men are suitable within the profession continues to be a divided issue. Men enter the profession for a multitude of reasons, yet barriers whether emotional, verbal or sexual are still present. Aim: The aim of this study was to examine the experience of men “training” to be registered nurses within a regional New Zealand context. Design: A Narrative Analysis approach was used. Participants: Five New Zealand men currently undertaking their bachelor of nursing degree at a regional tertiary institute were interviewed as to their experiences of what it meant to be a man in “training”. Method: A thematic analysis was undertaken and guided by an understanding of the way personal narratives informs the human sciences especially within the context of nursing praxis. Four key themes were identified. Results: Four key themes were identified: A career with flexibility and promise; perceived gender inequality in providing care; developing professional boundaries with female colleagues and being unique has its advantages. Conclusion: The men in this study were attracted to the profession by career stability and advancement; the opportunities for travel also figured highly. At times they felt excluded and marginalised because of their minority status within their group and the feminine nature of the curriculum. The men attempted to dispel the myth around male nurse sexual stereotypes. Some of the students behaved in a manner to exert their heterosexualness. The students in this study sensed their vulnerability in choosing nursing as a career. However, all the participants saw nursing as viable and portable career in terms of advancement and travel.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".