Male nurses’ mental health and provision of emotional support during COVID-19: a thematic analysis
Bibliographic record
Abstract
Background: COVID-19 has created mental health challenges for nurses. However, it is unknown if there is a gendered influence on nurses’ experiences during the pandemic. Aim: The aim of this study was to explore the experiences of male nurses during COVID-19, including their mental health and experiences of providing emotional support. Methods: We conducted semi-structured interviews using Zoom with nine male nurses and analysed the interviews using thematic analysis. Findings: Male nurses experienced negative mental health outcomes from the pandemic, but participants attributed these outcomes to the context. Male nurses provided emotional support for patients, students, families and other staff and did not describe emotional support as a difficult part of their work. Participants identified their role as a ‘breadwinner’ of being part of their concern during COVID-19. Participants used a variety of approaches to manage the stressors from the pandemic and cautioned against alcohol as a coping strategy. Discussion and Conclusions: Participants provided emotional support routinely as part of their work and went to great lengths to do so. This finding differs from most published literature that suggests male nurses struggle with emotional and interpersonal aspects of nursing. Male nurses require emotional support and employers can note that need for support may present differently by gender.
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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 | 0.012 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".