A Comparative Study of Australian and New Zealand Male and Female Nurses’ Health
Bibliographic record
Abstract
The aim of this research was to compare the health and lifestyle behaviors between male and female nursing professionals. Biological, workplace, and lifestyle factors as well as health behaviors and outcomes are reported as different between male and female nurses. Although male nurses show distinct health-related patterns and experience health disparities at work, few studies have investigated health differences by sex in a large cohort group of nursing professionals. This observation study of Australian and New Zealand nurses and midwives drew data from an eCohort survey. A cohort of 342 females was generated by SPSS randomization (total N=3625), to compare against 342 participating males. Measures for comparison include health markers and behaviors, cognitive well-being, workplace and leisure-time vitality, and functional capacity. Findings suggest that male nurses had a higher BMI, sat for longer, slept for less time, and were more likely to be a smoker than their female nurse counterparts. Men were more likely to report restrictions in bending, bathing, and dressing. In relation to disease, male nurses reported greater rates of respiratory disease and cardiovascular disease, including a three times greater incidence of myocardial infarction, and were more likely to have metabolic problems. In contrast, however, male nurses were more likely to report feeling calm and peaceful with less worries about their health. Important for nurse workforce administrators concerned about the well-being of their staff, the current study reveals significant sex differences and supports the need for gender-sensitive approaches to aid the well-being of male nurses.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".