Savoring Daily Life: Views of Aging as a Moderator of Savoring and Positive Affect
Bibliographic record
Abstract
Abstract Positive events are common in daily life (e.g., having a good conversation) and savoring strategies can be used to up-regulate positive emotions. Drawing from Socioemotional Selectivity Theory, individuals with relatively less positive views of aging (VoA) might attend more to positive and meaningful experiences in the present as a function of shifting time perspective. Therefore, we hypothesized that people with less positive VoA would derive more positive affect from savoring their positive experiences. This pre-registered study analyzed experience sampling data from an adult lifespan sample from Canada (n = 178 persons and 14 days, Mage=47) and an older adult sample from Switzerland (n = 108 persons and 7 days, Mage=73). VoA were measured through attitudes toward own aging and age stereotypes, while savoring and positive affect were assessed at the end of each day. Two-level multilevel models revealed that positive affect was higher on days with more-than-usual savoring (Canada sample: b = 0.16, 95% CI[0.03, 0.30], p=.019; Switzerland sample: b = 0.42, 95% CI[0.35, 0.49, p<.001). VoA moderated the link between savoring and positive affect in the Swiss sample only, such that individuals with less positive VoA had smaller upticks in positive affect associated with savoring (simple slope for -1 SD: b = 0.34, SE = 0.05, p<.001), compared to those with more positive VoA (simple slope for +1 SD: b = 0.49, SE = 0.05, p<.001). Findings suggest that older adults with more positive VoA may benefit more from savoring, but it remains unclear whether VoA hold the same importance for savoring among younger and middle-aged adults.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".