Factors affecting mental health and happiness in the elderly: A structural equation model by gender differences
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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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".