Bodies Matter: Men, Masculinity, and the Gendered Division of Labour in Nursing
Bibliographic record
Abstract
Background: Biological differences between the bodies of men and women play a major role in determining the type of work performed by them and they therefore contribute to the social construction of some kinds of work as more masculine than others. Within the numerically female‐dominated profession of nursing, the experiences of men provide insight into the ways men's bodies and notions of masculinity have contributed to a gendered division of labour. Purpose: This paper examines the experience of men in nursing and the roles men nurses expect and are expected to assume by virtue of being men. Findings: Data from interviews with eight men nurses registered in Nova Scotia, Canada reveal that roles such as ‘he‐man’ and enforcer create complex situations of advantage and disadvantage for men nurses. On the one hand, they affirm masculinity and men's special contribution to nursing. On the other hand, they generate extra work, compromise men nurses’ relationships with women nurses and women patients, and project an image of men as uncaring. Even more problematic, the ‘he‐man’ and enforcer roles may construct an additional role of ‘failed caregiver’. The experience of men in numerically female‐dominated occupations such as nursing points to the need to recognize the extent to which bodily qualities, abilities and practices have come to be seen as masculine or feminine. An additional challenge is to understand how this association manifests itself as a gendered division of labour that negatively impacts the work lives of men and women alike.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.023 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".