Are Loneliness and Social Isolation Equal Threats to Health and Well-being? An Outcome Wide Longitudinal Approach
Bibliographic record
Abstract
Abstract The detrimental effects of loneliness and social isolation on health and well-being outcomes are well documented. In response, governments, corporations, and community-based organizations have begun leveraging emerging tools to create interventions and policies aimed at reducing loneliness and social isolation at-scale. However, these efforts are frequently hampered by a key knowledge gap: when attempting to alleviate specific health and well-being outcomes, decision-makers are unsure whether to target loneliness, social isolation, or both. Participants (N=13,752) were from the Health and Retirement Study- a diverse nationally representative, and longitudinal sample of U.S. adults aged > 50 years. We examined how changes in loneliness and social isolation over a 4-year follow-up period (from t0:2008/2010 to t1:2012/2014) were associated with 32 indicators of physical-, behavioral-, and psychosocial-health outcomes 4-years later (t2:2016/2018). We used, multiple logistic-, linear-, and generalized-linear regression models, and adjusted for sociodemographics, personality traits, pre-baseline levels of both exposures (loneliness and social isolation), and all outcomes (t0:2008/2010). After adjusting for a wide range of covariates, we observed that both loneliness and social isolation have similar effects on physical health outcomes and health behaviors, whereas loneliness is a stronger predictor of psychological outcomes. In particular, behavioral dimensions of the social isolation measure (i.e., participation in social/religious activities, social interaction frequency) were most strongly associated with the largest number of health and well-being outcomes, including all-cause mortality. Loneliness and social isolation have independent effects on various health and well-being outcomes, thus, should be distinct targets for interventions aimed at improving the health and well-being.
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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 | 0.010 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".