A dyadic daily diary investigation of partner-schema structures on relational well-being and depressed mood
Bibliographic record
Abstract
Partner-schemas structures (i.e., the degree of interconnectedness in the beliefs one holds about their romantic partner) have been associated with relationship well-being and, to a lesser degree, depression, in cross-sectional research. However, little is known about how schema structures may impact couples in their daily lives. To address this gap, 260 couples were sampled at baseline to assess partner-schema structures, then surveyed for 14 days. Daily surveys captured relationship quality, relationship conflict severity, and relationship rumination; changes in relationship quality and relationship rumination on the day of a relational conflict; and depressed mood. Using multilevel modelling, guided by the Actor-Partner Interdependence Model, significant actor and partner effects emerged. Generally, the results suggest that an individual's partner-schema structures are significantly associated with their own daily relationship quality, relationship rumination, increased relationship rumination on the day of a relational conflict, and depressed mood. Two partner effects also emerged, indicating that one's own partner-schema structures may shape one's partner's relationship rumination on the day of a conflict, as well as their partner's depressed mood. Exploratory analyses also suggest that the linkages between conflict severity and daily relationship quality, daily relationship rumination, and daily depressed mood may be moderated by partner-schema structures. These findings extend previous cross-sectional research to the daily dyadic context. Implications for future research are discussed.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".