Beyond fun and friendship: the Red Hat Society as a coping resource for older women
Bibliographic record
Abstract
ABSTRACT How older women cope with challenges and losses in later life influences not only their physical health but also their psychological wellbeing and quality of life. The purpose of the analysis reported in this paper was to understand how participation in a women's leisure-based social group – the Red Hat Society ® – serves as a coping resource for older women. The Society is an international organisation of women aged 50 or more years and has the mission to ‘celebrate the silliness of life’. The Society currently has an estimated one million members in 30 countries. To understand the ways that social group participation may contribute to older women's health and wellbeing, this paper examines the dynamics of leisure-based coping with positive emotions as the focus. Based on an analysis of responses to an open-ended question about meaningful experiences associated with being involved in the Red Hat Society, the sample of 272 members identified the main reasons for their involvement as chronic and acute stressors, challenging life transitions and daily hassles. In addition, they described four ways that participation helped them to manage these stressors: as a context for social support, emotional regulation, sustaining coping efforts, and meaning-focused coping. The results are discussed in relation to theory and previous evidence on the role of positive emotions and leisure in coping.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".