The Importance of Social Groups for Retirement Adjustment: Evidence, Application, and Policy Implications of the Social Identity Model of Identity Change
Bibliographic record
Abstract
Abstract Previous work in the social identity tradition suggests that adjustment to significant life changes, both positive (e.g., becoming a new parent) and negative (e.g., experiencing a stroke), can be supported by access to social group networks. This is the basis for the social identity model of identity change (SIMIC), which argues that, in the context of life transitions, well‐being and adjustment are enhanced to the extent that people are able to maintain preexisting social group memberships that are important to them or else acquire new ones. Building on empirical work that has examined these issues in the context of a variety of life transitions, we outline the relevance of SIMIC for one particular life transition: retiring from work. We identify four key lessons that speak to the importance of managing social group resources effectively during the transition to retirement from the workforce. These suggest that adjustment to retirement is enhanced to the extent that retirees: (1) can access multiple important group memberships and the psychological resources they provide, (2) maintain positive and valued existing groups, and (3) develop meaningful new groups, (4) providing they are compatible with one another. This theory and empirical evidence is used to introduce a new social intervention, Groups 4 Health , that translates SIMIC's lessons into practice. This program aims to guide people through the process of developing and embedding their social group ties in ways that protect their health and well‐being in periods of significant life change of the form experienced by many people as they transition into retirement.
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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 | 0.020 | 0.071 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".