POSITIVE EVENT DIVERSITY AND AGE: TESTING CURVILINEAR ASSOCIATIONS
Bibliographic record
Abstract
Abstract Prior research has found that older adults report more frequent positive events compared to younger and middle-aged adults. It remains unknown, however, to what extent age is related to experiencing positive events spread across different positive event types (i.e., positive event diversity, PED). We therefore examined age differences in PED and its affective well-being correlates across three large daily diary datasets: wave 2 (N = 1919) and the Refresher sample (N = 744) of the National Studies of Daily Experiences, and the Coping with the COVID-19 Outbreak Study (N = 1392). We used Shannon’s Entropy to quantify PED. There was a curvilinear association between PED and age, indicating that PED was higher among middle-aged compared to younger and older adults. On a zero-order basis, PED was correlated with higher positive affect and uncorrelated with negative affect. However, the pattern of results changed when considering positive event frequency as a moderator. Among people with high positive event frequency, PED was linked to higher negative and lower positive affect, but not associated with either positive or negative affect at low levels of positive event frequency. These relationships did not differ by age. This study suggests that middle-aged adults experience the widest range of positive event types, perhaps reflecting the diverse set of social roles that middle-aged adults often fulfill (e.g., worker, parent, partner). In addition, there might be negative consequences to experiencing a lot of positive events across very different contexts which may indicate social role strain.
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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 | 0.008 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".