Factors affecting mental health and happiness in the elderly: A structural equation model by gender differences
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Bibliographic record
Abstract
PURPOSE: There are few studies on the gender differences in mental health, happiness, and their related factors among the older population through the structural equation model (SEM) in Iran. We conducted this study to evaluate the factors affecting mental health and happiness in the elderly using an SEM by gender differences. METHODS: A cross-sectional study was conducted on 739 elderly people in 2019 in Karaj, Iran. Sociodemographic, Symptom Checklist-90-Revised (SCL90-R), and the Oxford Happiness Inventory were applied to evaluate the relationships between happiness, mental health, and sociodemographic factors by using statistical path analysis with Lisrel 8.8 and SPSS-17. RESULTS: Overall, 55.5% of the participants in the study were female. The SCL90 (p value = .000) and happiness (p value = .000) scores showed significant differences between men and women. Fit indices confirmed the high model fitness, desirability, and logical relationships between the variables according to the conceptual model in both men (X2 = 3.2, df = 1) and women (X2 = 5.4, df = 2) groups. According to the path analysis, among the variables that affected happiness just through the direct path, education had the most positive causal relationship in men (B = .13) and women (B = .16), but mental health problems in men (B = -.33) and women (B = -.26), as well as the distance from home to the healthcare center in men (B = -.13) and women (B = -.11), had the most negative causal relationship with happiness respectively. Age was the only variable that was negatively related to happiness through direct and indirect paths in the women (B = -.188). CONCLUSION: We provided an empirical model that illustrates the relationships between happiness, mental health, and related factors in the older population. Gender differences in path analysis showed that age negatively affects the happiness of older women but not men.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.000 | 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 it