Chronic Stress as a Moderator of the Association between Depressive Symptoms and Marital Satisfaction
Bibliographic record
Abstract
In a two-year study of 190 newlywed couples, multi-level contemporaneous and time-lagged models indicated that marital satisfaction and depressive symptoms covaried over time, but only marital satisfaction predicted subsequent changes in depressive symptoms and depressive symptoms did not predict subsequent changes in marital satisfaction. Average levels of chronic stress moderated the contemporaneous association between marital satisfaction and depressive symptoms as an outcome; for husbands, higher average non-marital stress (but not marital stress) strengthened the association and for wives, higher marital stress (but not non-marital stress) strengthened the association. The contemporaneous association between depressive symptoms and marital satisfaction as an outcome strengthened when marital stress was higher (for wives only), but contrary to prediction, the association weakened for both spouses when non-marital chronic stress was higher. Chronic stress (marital or non-marital) did not moderate time-lagged associations. Results highlight the role of marital and broader social contexts on the reciprocal associations between marital satisfaction and depressive symptoms.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| 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 | 0.000 | 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".