Predictors of Health Promoting Lifestyle Among Midwives Employed in Hospitals and Health Centres of Qazvin, Iran
Bibliographic record
Abstract
Background and aims: Midwives experience a high level of stress due to heavy workloads, which has been shown to have adverse effects on well-being. Accordingly, the main goal of this study was to assess the predictors associated with a healthy lifestyle in a sample of midwives working in hospitals and health centers of Qazvin, Iran. Methods: A total of 200 midwives were recruited using convenience sampling method. Each subject completed a demographic questionnaire, the Farsi version of the Health Promoting Lifestyle Profile Questionnaire, and Perceived Social Support Questionnaire. A multivariate linear regression model was used to assess the predictors of health promoting lifestyle (HPL). Results: Spiritual growth (2.78±0.53) and nutrition (2.79±0.45) had the highest scores among HPL subscales. Conversely, subjects had the lowest score in physical activity (2.02±0.64). Multivariate regression analyses showed that workplace (β=-0.19, P =0.03), involving in professional sports (β=0.2, P=0.01), and perception of an adequate social support network (β=0.47, P <0.001) were the strongest predictors of HPL. These predictors accounted for 27% of the variance in the model. Conclusion: Considering the predictive role of three variables including workplace, involving in professional sport, and having adequate social support, HPL interventions can be designed and implemented. Improving working conditions, strengthening social support networks, and increasing physical activity might be beneficial measures to improve midwives’ HPL.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.043 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".