Emotional Suppression and Psychological Well-Being in Marriage: The Role of Regulatory Focus and Spousal Behavior
Bibliographic record
Abstract
Emotional suppression has been considered a critical factor in determining one's mental health and psychological well-being in intimate relationships such as marriage. The present study aimed to delineate the nuanced association between emotional suppression and psychological well-being in marriage by considering two critical factors: (a) individual differences in motivational orientation and (b) the perceived level of a partner's emotional suppression. A set of two online survey studies were conducted on a large sample of married participants. The participants were asked to indicate (a) their own level of emotional suppression, (b) the perceived level of their spouse's emotional suppression, (c) relationship motivation, and (d) satisfaction with marital life. The results consistently indicated that for prevention-focused individuals being emotionally suppressive was associated with greater marital satisfaction, but only for those who perceived their spouses as also emotionally suppressive. Conversely, for promotion-focused individuals, being less emotionally suppressive was associated with greater marital satisfaction, but again, only for those who perceived their spouses as also being less emotionally suppressive. These findings provide insights into research on emotion regulation and self-regulatory strategies in influencing psychological well-being and mental health in an intimate relationship.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".