AFFECT MODERATES THE ASSOCIATION BETWEEN SOCIAL SUPPORT AND RETIREMENT SATISFACTION OVER TIME
Bibliographic record
Abstract
Abstract Retirement is becoming more important for today’s older adults because they are living longer than before. Recently, research has started to explore how different individual resources (e.g., health or finances) and social resources (e.g., social support or social network size) influence retirement outcomes such as retirement satisfaction. Moreover, the current study sought to examine the influence of time, satisfaction with social support, and affect (i.e., positive or negative) as predictors of retirement satisfaction. Data was obtained from a longitudinal study that explored how older adults in Montreal, Canada adjusted to life in retirement over the course of three years. Hypotheses were tested using a structural equation model that investigated retirement satisfaction as predicted by time, satisfaction with social support, positive affect, and negative affect. Gender differences were also explored. Overall, there was no change over time among the variables. Satisfaction with social support, positive affect, and negative affect were all associated with retirement satisfaction in the expected directions. Positive affect moderated the association between satisfaction with social support and retirement satisfaction, such that the association was stronger for those low in positive affect. Also, negative affect moderated the association between satisfaction with social support and retirement satisfaction as a function of gender. This study extended the literature by exploring how multiple predictors interacted to influence retirement satisfaction over time. Future research should examine how individual and social resources can interact with each other to better understand retirement satisfaction.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".