Emotion management and stereotypes about emotions among male nurses: a qualitative study
Bibliographic record
Abstract
BACKGROUND: Nursing requires a high load of emotional labour. The link between nursing, emotional labour and the female sex, complicates the figure of the male nurse, because masculinity is associated with physical or technical (rather than emotional) and moreover is defined in contrast to femininity. Our objective was to understand how emotion management is described by male nurses who work in the paediatrics department of a Spanish tertiary hospital. METHODS: Qualitative descriptive study. The participants were selected through intentional sampling in the paediatrics department of a Spanish tertiary hospital. We conducted semi-structured interviews until reaching data saturation. We carried out a content analysis, using Lincoln and Guba's definition of scientific rigour. RESULTS: We identified two key themes in the data: 1) Stereotypes related to the emotional aspects of care: Participants took for granted some gender stereotypes while questioning others and defended alternative ways of managing emotions related to care. 2) Emotion management strategies: Participants described keeping an emotional distance, setting boundaries, relativising problems and using distraction and humour. DISCUSSION: Nursing care is conditioned by gender roles and stereotypes that present men as less capable than women of feeling and managing emotions. However, emotion management is necessary in nursing care-especially in paediatrics-and our participants reported using strategies for it. Although participants continued to interpret care in terms of traditional roles, they contradicted them in adapting to the emotional labour that their job requires. CONCLUSIONS: New behaviours are emerging among male nurses, in which care and emotion management are not exclusively the purview of women. Our participants reproduced some gender stereotypes while disrupting others, and they tended to cling to the stereotypes that were favourable to them as male nurses. As we work towards a gender-neutral profession, these results represent a first step: male participants reported that they provide care and manage their emotions as well as (or better than) women. However, because they substantiated their claims by drawing on negative stereotypes of women, further progress must be made.
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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 | 0.014 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".