Caught in a bad romance: Perfectionism, conflict, and depression in romantic relationships.
Bibliographic record
Abstract
According to the social disconnection model, perfectionistic concerns (i.e., harsh self-scrutiny, extreme concern over mistakes and others' evaluations, and excessive reactions to perceived failures) confer vulnerability to depressive symptoms indirectly through interpersonal problems. This study tested the social disconnection model in 226 heterosexual romantic dyads using a mixed longitudinal and experience sampling design. Perfectionistic concerns were measured using three partner-specific self-report questionnaires. Conflict was measured as a dyadic variable, incorporating reports from both partners. Depressive symptoms were measured using a self-report questionnaire. Perfectionistic concerns and depressive symptoms were measured at Day 1 and Day 28. Aggregated dyadic conflict was measured with daily online questionnaires from Days 2 to 15. Data were analyzed using structural equation modeling. There were four primary findings: (a) Dyadic conflict mediated the link between perfectionistic concerns and depressive symptoms, even when controlling for baseline depressive symptoms; (b) depressive symptoms were both an antecedent and a consequence of dyadic conflict; (c) perfectionistic concerns incrementally predicted dyadic conflict and depressive symptoms beyond neuroticism (i.e., a tendency to experience negative emotions) and other-oriented perfectionism (i.e., rigidly demanding perfection from one's partner); and (d) the relationships among variables did not differ based on gender. As the most rigorous test of the social disconnection model to date, this study provides strong support for this emerging model. Results also clarify the characterological and the interpersonal context within which depressive symptoms are likely to occur.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".