PERSONALITY, SOCIAL ISOLATION, AND LONELINESS: CROSS-SECTIONAL DATA FROM THE CANADIAN LONGITUDINAL STUDY ON AGING
Bibliographic record
Abstract
Abstract Good social connection is associated with better health. However, the relationships between distinct aspects of objective and subjective social connection, and how personality traits may impact these relationships, are not well-understood. For example, in predicting loneliness, personality could moderate the impact of objective social connection by influencing the way social relationships are evaluated. We used cross-sectional Canadian Longitudinal Study on Aging data (n=27,765), collected between 2015 and 2018, to explore the potential confounding or modifying effects of personality traits on the associations between social isolation and loneliness score among 46-90 year-olds. We estimated associations using ordered logistic regression models, whereby the outcome was the loneliness score and exposures were social isolation and each of five personality traits (extraversion, agreeableness, conscientiousness, emotional stability, and openness). We produced three sets of models (social isolation only; personality trait only; and social isolation, personality trait and their interaction) and conducted all analyses stratified by sex and adjusting for potential confounders. We found that, for both males and females, social isolation and each of the five personality traits were associated with loneliness scores (p< 0.001), however, with only one exception (openness among females), there was no statistical evidence that personality modified the association between social isolation and loneliness. Our findings have potential implications for research assessing the health effects of social connection and personality as well as intervention studies that target social isolation as means of addressing loneliness.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".