Cautious caregivers: gender stereotypes and the sexualization of men nurses' touch
Bibliographic record
Abstract
AIM: The aim of this research was to explore the experience of men nurses and the ways in which gender relations structure different work experiences for women and men in the same profession. BACKGROUND: Men are now entering the nursing profession in record numbers and challenging the notion that men are inappropriate in caregiver roles or incapable of providing compassionate and sensitive care. A limitation of the current state of knowledge regarding caring and men nurses is that it is primarily focused on men nursing students, not practising nurses. Little is known about men nurses' practices of caring and how such practices reflect the gendered nature of nursing and nurses' caring work. METHODS: The theme of men nurses as cautious caregivers emerged from data that were collected in two rounds of semi-structured interviews with eight men nurses practising in Nova Scotia, Canada. Thematic analysis, informed by feminist theory and masculinity theory, was used as the method for analysing the data. FINDINGS: For men nurses, the stereotype of men as sexual aggressors is compounded by the stereotype that men nurses are gay. These stereotypes sexualize men nurses' touch and create complex and contradictory situations of acceptance, rejection and suspicion of men as nurturers and caregivers. They also situate men nurses in highly stigmatized roles in which they are subject to accusations of inappropriate behaviour. For men nurses, this situation is lived as a heightened sense of vulnerability and the continual need to be cautious while touching and caring for patients. Ultimately, this situation impacts on the ability of men nurses to do the caring work they came into nursing to do.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.009 | 0.015 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| 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".