Beyond main effects? Affect level as a moderator in the relation between affect dynamics and depressive symptoms
Bibliographic record
Abstract
There are opposing views on the relative adaptiveness of affective change and its role in depression. Some studies link depression to more changes in affect (i.e., higher variability), others to a greater resistance to change (i.e., higher inertia). Our study provides an explanation for such inconsistencies by hypothesizing that the relation between affect dynamics and depression differs as a function of mean levels of affect. We analyzed data from seven studies that measured affect in daily life in adolescents and young adults (N = 1,448, age range = 11.7-29.9 years, 64.8% females). We tested main and interaction effects of affect dynamics (variability and inertia) and affect level on depressive symptoms, separately for positive affect (PA) and negative affect (NA). For PA, we found mostly main, but no interaction effects. Depressive symptoms were associated with more PA variability and less PA inertia, indicating that depressive symptoms in young people may be characterized by more fluctuating PA, independent of PA mean levels. For NA, we found a significant moderation effect between NA variability and NA levels for depressive symptoms at baseline. For individuals with low NA levels, high NA variability was associated with more depressive symptoms. In contrast, for individuals with high NA levels, high NA variability was associated with fewer depressive symptoms. These results suggest that the relative adaptiveness of NA variability depends on overall NA levels and underscores the need for a more nuanced understanding of affect variability in depression.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".