Actor–partner effects of perfectionism on marital satisfaction: Self‐disclosure as an interpersonal mechanism
Bibliographic record
Abstract
Abstract Object The current study examines whether and how self‐disclosure, the voluntary sharing of personal thoughts and feelings, explains the behavioral processes through which two dimensions of perfectionism, the drive for perfection (i.e., perfectionistic strivings) and worry about imperfection (i.e., perfectionistic concerns), are associated with both partners' marital satisfaction in different ways. Background Although many studies identified the contrasting effects of different dimensions of perfectionism on relationship quality, only a few clearly examined the interpersonal mechanism underlying their distinct effects in a dyadic context. This study aimed to address this research gap. Method The study recruited a dyadic sample of 158 mixed‐gender married couples from South Korea to investigate their levels of (a) perfectionistic strivings and concerns, (b) self‐disclosure, and (c) marital satisfaction using a pencil‐and‐paper survey. Data were analyzed using the actor–partner interdependence mediation model (APIMeM) within a structural equation modeling framework. Results Perfectionistic strivings were associated with being more open to sharing personal thoughts and feelings (i.e., higher self‐disclosure), which in turn predicted higher marital satisfaction for the person exhibiting them and their partner. Conversely, perfectionistic concerns were linked to being less likely to share personal thoughts and feelings, which in turn predicted lower marital satisfaction for both partners. Conclusion These results reveal that each partner's self‐disclosure significantly explained the processes by which perfectionistic strivings and concerns predicted higher or lower levels of couples' marital satisfaction, respectively.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".