Modelling Age-Varying Associations among Group Memberships, Neighborhood Connectedness, and Well-Being
Bibliographic record
Abstract
Individuals who maintain group memberships in their community tend to experience improved well-being relative to those who participate in few or no groups. There are, however, few investigations targeting variability in the correlates of group membership across the lifespan. The present examination probed age-related variability in the association between group memberships and subjective connectedness as well as well-being. Participants included 3,940 (mean age = 45.61 years, standard deviation [SD] = 15.62) Canadian and American respondents who completed an online survey during August of 2020 (i.e., amidst the COVID-19 pandemic). Time-varying effects modelling was used to estimate coefficients for group membership at each age within the sample. Memberships in social groups positively predicted connectedness, and this association was strongest in middle-to-older age; a similar association was also evident when predicting well-being. Connectedness was also a positive predictor of well-being throughout most ages. These findings build on emerging research conveying how group memberships have significance for people currently in middle-to-older age.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".