THE TEMPORAL SEQUENCE OF FRAILTY, SOCIAL ISOLATION, AND LONELINESS IN OLDER ADULTS ACROSS 21 YEARS
Bibliographic record
Abstract
Abstract This study examined the temporal patterns of social isolation, loneliness, and frailty among a national sample of older adults across 21 years (1995–2016). We applied random intercept cross-lagged panel models to seven waves of the Longitudinal Aging Study Amsterdam (LASA). We included 2,302 Dutch older adults ages 55 and older (Mage= 72.6; SD = 8.6; 52.1% female). Frailty was measured using the Frailty Index. Loneliness was measured using the 11-item De Jong Gierveld loneliness scale. Social isolation was measured using a multi-domain 6-item scale. Levels of social isolation and loneliness were weakly correlated during any given wave as well as across waves. Each condition was highly stable from one time point (T) to the next. Cross-lagged effects showed that frailty in T2 to T5 significantly predicted social isolation in the following waves (T3 to T6). For example, higher levels of frailty at T4 predicted higher levels of social isolation at T5 (β = 0.23, SD = 0.05, p < 0.001). Likewise, frailty in T2 to T6 significantly predicted loneliness in the following waves (T3 to T7). For example, higher levels of frailty at T5 predicted higher levels of loneliness at T6 (β = 0.33, SD = 0.06, p < 0.001). The reverse sequence (i.e., social isolation or loneliness predicting future frailty) was less common and exhibited a weaker relationship. Frailty may have important consequences for adverse psychosocial outcomes, including social isolation and loneliness. Public health policies should prioritize interventions that bolster social connection among pre-frail and frail older adults.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".