Examining individual and geographic factors associated with social isolation and loneliness using Canadian Longitudinal Study on Aging (CLSA) data
Bibliographic record
Abstract
BACKGROUND: A large body of research shows that social isolation and loneliness have detrimental health consequences. Identifying individuals at risk of social isolation or loneliness is, therefore, important. The objective of this study was to examine personal (e.g., sex, income) and geographic (rural/urban and sociodemographic) factors and their association with social isolation and loneliness in a national sample of Canadians aged 45 to 85 years. METHODS: The study involved cross-sectional analyses of baseline data from the Canadian Longitudinal Study on Aging that were linked to 2016 census data at the Forward Sortation Area (FSA) level. Multilevel logistic regression analyses were conducted to examine the association between personal factors and geographic factors and social isolation and loneliness for the total sample, and women and men, respectively. RESULTS: The prevalence of social isolation and loneliness was 5.1% and 10.2%, respectively, but varied substantially across personal characteristics. Personal characteristics (age, sex, education, income, functional impairment, chronic diseases) were significantly related to both social isolation and loneliness, although some differences emerged in the direction of the relationships for the two measures. Associations also differed somewhat for women versus men. Associations between some geographic factors emerged for social isolation, but not loneliness. Living in an urban core was related to increased odds of social isolation, an effect that was no longer significant when FSA-level factors were controlled for. FSAs with a higher percentage of 65+ year old residents with low income were consistently associated with higher odds of social isolation. CONCLUSION: The findings indicate that socially isolated individuals are, to some extent, clustered into areas with a high proportion of low-income older adults, suggesting that support and resources could be targeted at these areas. For loneliness, the focus may be less on where people live, but rather on personal characteristics that place individuals at risk.
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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 | 0.001 | 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 | 0.001 | 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".