Benefits of Capitalization in Newlyweds: Predicting Marital Satisfaction and Depresion Symptoms
Bibliographic record
Abstract
Celebrating good news with others (i.e., capitalization) is associated with positive affect and relationship well-being. However, it remains unclear whether the benefits of capitalization result in enduring changes in personal and relationship well-being, and whether contextual factors (i.e., chronic stress) moderate the value of positive capitalization experiences. We examined contemporaneous and time-lagged associations between capitalization perceptions and marital satisfaction or depression symptoms in 192 newlywed couples over two years. We also examined whether chronic stress moderated the effects of capitalization perceptions on depression symptoms and marital satisfaction. Multilevel analyses indicated that capitalization perceptions predicted contemporaneous and time-lagged changes in marital satisfaction for husbands and wives and contemporaneous and time-lagged changes in wives’, but not husbands’, depression symptoms. In other words, the more positively spouses viewed their partner's responses, the more maritally satisfied they became, and the less likely wives were to experience depression symptoms. Further, as wives’ chronic stress increased, there was a stronger negative association between capitalization perceptions and contemporaneous depression symptoms. Results highlight how celebrating successes and good fortune can set spouses on a trajectory toward personal and relationship well-being.
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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 | 0.000 | 0.002 |
| 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) | 0.002 | 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".