Gender difference about death anxiety among older adults: Structural Equation Model
Bibliographic record
Abstract
BACKGROUND: Older adults may be more prone to death anxiety than their younger counterparts. This study explores factors affecting death anxiety based on gender differences. METHODS: In this correlational study, 450 older adults referred to the health centres in the city of Bukan, Iran were recruited by using a randomised sampling method. Next, data were collected about the demographic questionnaire, anxiety about ageing, death anxiety, mental well-being, perceived social support, and quality of life questionnaire. The Spearman correlation coefficient was used to determine the correlation between variables, and the predictors of death anxiety were evaluated using quintile regression. Relationship between death anxiety and other variables was evaluated by the Structural Equation Model (SEM). The study was approved by the Tabriz University of Medical Sciences Ethics Committee (Ethics Code: IR.TBZMED.REC.1397.304). RESULTS: The results showed that death anxiety in men had a significant relationship with the level of literacy (P = 0.047), body self-imaging (P = 0.031), and perceived social activity (P = 0.033). Among women, death anxiety had a significant relationship with physical activity (P = 0.007) and perceived social activity (P = 0.002). Additionally, quintile regression analysis was calculated: among men, anxiety about ageing was related to death anxiety (β = 0.182, P = 0.05), while in women, only perceived social support was associated to death anxiety (β = -0.376, P = 0.05). Finally, according to SEM, a significantly different level of predictability of mental well-being was found for death anxiety among older men and women. CONCLUSION: Understanding the gender differences about death anxiety by the healthcare system might be useful in controlling and reducing a variety of concerns among elders who experience high levels of anxiety of death.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".