Affect variability and physical health: The moderating role of mean affect
Bibliographic record
Abstract
Research has only begun to explore how affect variability relates to physical health and has typically not assessed long-term associations nor considered the moderating role of mean affect. Therefore, we used data from the Midlife in the United States Study waves 2 (N = 1512) and 3 (N = 1499) to test how affect variability predicted concurrent and long-term physical health while also testing the moderating role of mean affect. Results indicated that greater negative affect variability was associated concurrently with a greater number of chronic conditions (p = .03) and longitudinally with worse self-rated physical health (p < .01). Greater positive affect variability was associated concurrently with more chronic conditions (p < .01) and medications (p < .01) and longitudinally with worse self-rated physical health (p = .04). Further, mean negative affect played a moderating role such that at lower levels of mean negative affect, as affect variability increased, so did the number of concurrent chronic conditions (p < .01) and medications (p = .03) and the likelihood of reporting worse long-term self-rated physical health (p < .01). Thus, the role of mean affect should be considered when testing short- and long-term associations between affect variability and physical health.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".