The silent epidemic of loneliness: identifying the antecedents of loneliness using a lagged exposure-wide approach
Bibliographic record
Abstract
Abstract Background A large and accumulating body of evidence shows that loneliness is detrimental for various health and well-being outcomes. However, less is known about potentially modifiable factors that lead to decreased loneliness. Methods We used data from the Health and Retirement Study to prospectively evaluate a wide array of candidate predictors of subsequent loneliness. Importantly, we examined if changes in 69 physical-, behavioral-, and psychosocial-health factors (from t0;2006/2008 to t1;2010/2012) were associated with subsequent loneliness 4 years later (t2;2014/2016). Results Adjusting for a large range of covariates, changes in certain health behaviors (e.g. increased physical activity), physical health factors (e.g. fewer functioning limitations), psychological factors (e.g. increased purpose in life, decreased depression), and social factors (e.g. greater number of close friends) were associated with less subsequent loneliness. Conclusions Our findings suggest that subjective ratings of physical and psychological health and perceived social environment (e.g. chronic pain, self-rated health, purpose in life, anxiety, neighborhood cohesion) are more strongly associated with subsequent loneliness. Yet, objective ratings (e.g. specific chronic health conditions, living status) show less evidence of associations with subsequent loneliness. The current study identified potentially modifiable predictors of subsequent loneliness that may be important targets for interventions aimed at reducing 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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".