A Study of Role Stress, Organizational Commitment and Intention to Quit Among Male Nurses in Southern Taiwan
Bibliographic record
Abstract
Gender and sex role stereotyping are recognized as having the potential to limit the professional development of males within the nursing profession. The purpose of this study was to understand the relationships between demographic data and the dimensions of role stress, organizational commitment, and intentions to quit among male nurses in southern Taiwan. Research also investigated the correlations with three dependent variables and identified best predictors of male nurse intentions to quit the nursing profession. A total of 91 male nurses volunteered to participate in this cross-sectional research. Research results were based on data collected from questionnaires sent by mail to participants. A total of 76 valid questionnaires were returned and used in analysis (response rate = 83.5%). Findings pointed to patients, colleagues and society as the major sources of role stress for male nurses. These sources of stress, and the resultant intention to quit on the part of male nurses, are due in significant part to the widespread stereotyping of the profession of nursing as a "woman's occupation". Such stress pressures male nurses to consider quitting to take jobs in other professional fields. Role stress is correlated to intention to quit among male nurses. Role stress and years of service are highly relevant predictors of male nurse intention to quit and leave the nursing profession, explaining 33.8% of variability. We suggest that at various levels of education and society, promotion of male and female equality should be increased. There is also a need for psychological consultation as well as the promotion of male nurse role models to prevent male nurses turning away from nursing careers.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".