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 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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".