Mean Affect Moderates the Association between Affect Variability and Mental Health
Bibliographic record
Abstract
Abstract Increasing evidence suggests that within-person variation in affect is a dimension distinct from mean levels along which individuals can be characterized. This study investigated affect variability’s association with concurrent and longitudinal mental health and how mean affect levels moderate these associations. The mental health outcomes of depression, panic disorder, self-rated mental health, and mental health professional visits from the second and third waves of the Midlife in the United States Study were used for cross-sectional (n = 1,676) and longitudinal outcomes (n = 1,271), respectively. These participants took part in the National Study of Daily Experiences (NSDE II), where they self-reported their affect once a day for 8 days, and this was used to compute affect mean and variability. Greater positive affect variability cross-sectionally predicted a higher likelihood of depression, panic disorder, mental health professional use, and poorer self-rated mental health. Greater negative affect variability predicted higher panic disorder probability. Longitudinally, elevated positive and negative affect variability predicted higher depression likelihood and worse self-rated mental health over time, while greater positive affect variability also predicted increased panic disorder probability. Additionally, mean affect moderated associations between variability and health such that variability-mental health associations primarily took place when mean positive affect was high (for concurrent mental health professional use and longitudinal depression) and when mean negative affect was low (for concurrent depression, panic disorder, self-rated mental health, and longitudinal self-rated mental health). Taken together, affect variability may have implications for both short- and long-term health and mean levels should be considered.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".