The Moderating Effects of Stress and Rumination on Depressive Symptoms in Women and Men
Bibliographic record
Abstract
Although there is an abundance of research linking stress and rumination to depression in women, little is known with respect to the role stress plays in the relationship between rumination and depression. Moreover, the role of stress in the rumination-depression relationship has not been previously investigated separately in women. In the present study, 301 undergraduate women and 109 undergraduate men were administered a questionnaire battery to assess their degrees of stress, depressive symptoms and ruminative tendencies. Individually, both stress and rumination scores were found to account for a large proportion of variance in depressive symptom scores. The interaction of stress and rumination also accounted for a significant proportion of this variance, suggesting a significant moderating effect of stress on the rumination-depressive symptom relationship in women and men. Furthermore, women and men with the highest degrees of stress demonstrated the strongest rumination-depressive symptom relationship. However, low-stress women and low-stress men demonstrated divergent patterns of relationships. The alternative model of rumination as a moderator of the stress-depression relationship likewise supported divergent relationships between low-rumination women and low-rumination men in the relationship between stress and depression. The implications of these findings regarding vulnerability to depressive symptoms are discussed.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 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".