The Hypervigilance for Social Threats Predicts Older Adults’ Loneliness in Japan
Bibliographic record
Abstract
Objective：Present study aimed at examining the potential mechanisms are involved in the maintenance of loneliness in Japanese older adults. Methods：372 older adults (Mage＝70.17 years, SD＝4.69) were completed self-report measures of loneliness, interpersonal trust, rejection sensitivity, social withdrawal, and interpersonal distance. Results：Hierarchical multiple regression analysis revealed that interpersonal trust, and social withdrawal were significantly contribute to loneliness, and rejection sensitivity slightly positively predicted loneliness. A structural equation model further showed that older adults with greater hypervigilance for social threats (with lower interpersonal trust and greater rejection sensitivity) contribute to an increase of interpersonal distance to non-intimate persons and a stronger tendency of social withdrawal behavior, resulting in greater loneliness. Discussion： It showed that social threats-related cognition biases contributed to greater social withdrawal behavior, and fall into the depth of prolonged loneliness in older adulthood.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".