Self-Efficacy Beliefs as Predictors of Loneliness and Psychological Distress in Older Adults
Bibliographic record
Abstract
Sociodemographic variables, social support, and physical health have been used previously in a few predictor models of loneliness and psychological distress in late life. The present study, however, was designed to test the hypothesis that self-efficacy beliefs of elderly persons are significantly stronger predictors of loneliness and psychological distress than are demographics, social support, and physical health variables used in earlier predictor models. A sample of 141 women and 101 men, aged 65 to 86, reporting awide range of health status from "poor" to "excellent" was drawn from the region of Southern Alberta. Standard self-report measures were used to assess perceived self-efficacy in eight different domains. Findings from a series of hierarchical regression analyses that were conducted separately for men, women, and the combined sample supported the hypothesis concerning the superiority of the self-efficacy variables as predictors of loneliness and psychological distress. Gender-specific variations revealed that women's stronger self-efficacy domains in the interpersonal, social, and emotional realms, and men's stronger self-efficacy beliefs in the instrumental, financial, and physical realms predicted less loneliness and psychological distress. Spiritual self-efficacy emerged as being the most potent predictor, accounting for the largest percentage of explained variance in loneliness and psychological distress in the women's and combined sample. Implications of the findings are discussed for geriatric practitioners and clinicians.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".