The Predictive Factors of the Elderly Social Support in Tehran City, 2017
Bibliographic record
Abstract
Background/objective: social support is one of the most important aspects of the life of the elderly which shows the amount of enjoying from love, help, and attention of family members, friends, and others. Therefore, the present study aims at determining the predictors of the amount of social support in the elderly of Tehran. Methods: this was a descriptive-analytic study; using cluster sampling, it was conducted on 400 elderly people visiting the parks in Tehran five districts in 2017. The personal information questionnaire and “the Canadian Community Health Survey (CCHS)-Social Support questionnaire” were used for data collection after confirming their reliability and validity. Data were analyzed using SPSS 20 software, descriptive statistics, T-test, Pearson correlation, and regression analysis. Result: the average age and the elderly social support scores were 69.1 (±7.09) and 75.33 (±20.53). The results of multiple linear regression indicated that the variables of life companions (B=3.41), marital status (B=2.47), and housing status (B=-2.87) are regarded as predicting variables of social support. Conclusion: the married elderly, those who live with their spouse and children or those owning a personal house are more socially supported than the other elderly people. The educational level and the number of children did not have a significant relationship with the amount of social support. It seems that increased support for the elderly caregivers, training specialist forces for the elderly and organizing family consultation for the elderly and their family are effective in increasing emotional support.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".