The Effectiveness of Lifestyle Educational Program in Health Promoting Behaviors and Menopausal Symptoms in 45-60-Year-Old Women in Marvdasht, Iran
Bibliographic record
Abstract
INTRODUCTION: Health and lifestyle of women are of great importance in some periods of life, such as menopause. Since postmenopausal women are considered as a vulnerable group of the society, finding a strategy to improve their health seems necessary. METHODS: This experimental study with pretest-posttest design was carried out on 200 postmenopausal women between 45 and 60 years old in Ramjerd, Marvdasht, Iran, in 2014. The women who met the inclusion criteria of the study were selected by simple random sampling. The data were collected using demographic information questionnaire, Menopause Rating Scale (MRS), and Walker's Health Promoting Lifestyle Profile (HPLPII). The data were entered into the SPSS statistical software (version 19) and were analyzed using descriptive statistics, paired t-test, independent t-test, and chi-square test. P-values less than 0.05 were considered as statistically significant. RESULTS: The results of paired t-test showed a significant difference in the mean scores of health promoting lifestyle and MRS in the experimental group (P<.05), but not in the control group (P>.05). CONCLUSION: Lifestyle education was effective in health promoting behaviors and menopausal symptoms. After the educational intervention, health promoting behaviors increased and menopausal symptoms decreased in the postmenopausal women. Therefore, educational interventions based on health promoting lifestyles can be used as an appropriate strategy to reduce postmenopausal women's menopausal symptoms and improve their health.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".