Are loneliness and social isolation equal threats to health and well-being? An outcome-wide longitudinal approach
Bibliographic record
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 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 improve specific health and well-being outcomes, decision-makers are often unsure whether to target loneliness, social isolation, or both. Filling this knowledge gap will inform the development and refinement of effective interventions. Using data from the Health and Retirement Study (13,752 participants (59% women and 41% men, mean [SD] age = 67 [10] 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 sociodemographic, personality traits, pre-baseline levels of both exposures (loneliness and social isolation), and all outcomes (t0:2008/2010). We incorporated data from all participants into the overall estimate, regardless of whether their levels of loneliness and social isolation changed from the pre-baseline to baseline waves. After adjusting for a wide range of covariates, we observed that both loneliness and social isolation were associated with several physical health outcomes and health behaviors. However, social isolation was more predictive of mortality risk and loneliness was a stronger predictor of psychological outcomes. Loneliness and social isolation have independent effects on various health and well-being outcomes and thus constitute distinct targets for interventions aimed at improving population 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 | 0.011 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| 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".