Abide with me: religious group identification among older adults promotes health and well-being by maintaining multiple group memberships
Bibliographic record
Abstract
OBJECTIVES: Aging is associated with deterioration in health and well-being, but previous research suggests that this can be attenuated by maintaining group memberships and the valued social identities associated with them. In this regard, religious identification may be especially beneficial in helping individuals withstand the challenges of aging, partly because religious identity serves as a basis for a wider social network of other group memberships. This paper aims to examine relationships between religion (identification and group membership) and well-being among older adults. The contribution of having and maintaining multiple group memberships in mediating these relationships is assessed, and also compared to patterns associated with other group memberships (social and exercise). METHOD: Study 1 (N = 42) surveyed older adults living in residential care homes in Canada, who completed measures of religious identity, other group memberships, and depression. Study 2 (N = 7021) longitudinally assessed older adults in the UK on similar measures, but with the addition of perceived physical health. RESULTS: In Study 1, religious identification was associated with fewer depressive symptoms, and membership in multiple groups mediated that relationship. However, no relationships between social or exercise groups and mental health were evident. Study 2 replicated these patterns, but additionally, maintaining multiple group memberships over time partially mediated the relationship between religious group membership and physical health. CONCLUSION: Together these findings suggest that religious social networks are an especially valuable source of social capital among older adults, supporting well-being directly and by promoting additional group memberships (including those that are non-religious).
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.004 | 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".