Caught in a bad romance: Perfectionism, conflict, and depression in romantic relationships.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it