Health Promoting Lifestyle Behaviors in Menopausal Women: A Cross-Sectional Study
Bibliographic record
Abstract
BACKGROUND: Determining health promoting lifestyle behaviors of age-specific groups of women provides valuable information for designing health promotion intervention programs. Hence the present study was conducted to assess health promoting lifestyle behaviors in menopausal women. METHODS: The present descriptive cross-sectional study examined health promoting lifestyle behaviors in 400 menopausal women admitted to health care centers in Neka city-north of Iran-from March 2015 to July 2015. Health promoting lifestyle behaviors were evaluated using a demographic characteristics form and the Health Promoting Lifestyle Profile II (HPLP II) through simple convenience sampling. Data were analyzed in SPSS version 18 using descriptive and inferential statistics at the significance level of P<0.05. RESULTS: The mean score of participants' health promoting lifestyle behaviors was 136.43±19.61, ranging from 88 to 194. The logistic regression test revealed women's health promoting lifestyle behaviors to be significantly related to their place of residence (P=0.009, odds ratio=1.73) and their spouse's level of education (P=0.027, odds ratio=0.58). The Pearson correlation test showed significant relationships between mean score of the six sub-scale of health promoting lifestyle behaviors with each other (P<0.001). CONCLUSION: These findings have implications for addressing the role of men to promote health promoting lifestyle behaviors among rural menopausal women.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".