When We’re Asked to Change: The Role of Suppression and Reappraisal in Partner Change Outcomes
Bibliographic record
Abstract
Receiving a request to change from a romantic partner can evoke intense emotional responses that hinder change progress and conflict resolution. As such, investigating how those being asked to change (i.e., change targets) regulate their emotions through key intrapersonal strategies (i.e., suppression and reappraisal) will lend crucial insight into promoting change success. Utilizing laboratory-interaction (Study 1; N = 111 couples) and experience-sampling methods (Study 2; N = 2178 weekly reports from an 8-week diary), we assessed targets’ regulation strategies, change progress, and the extent to which they met their partner’s ideals. Preregistered analyses demonstrated that targets’ use of suppression was not linked to better or worse change outcomes. However, targets’ use of reappraisal was linked to better change outcomes as rated by both partners. Additional analyses revealed that targets’ suppression was linked to targets meeting their partner’s ideals more in the short term but less over time, whereas targets’ reappraisal was linked to targets meeting their partner’s ideals more in both the short term and over time. These findings highlight reappraisal as a key strategy for promoting successful partner change.
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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 | 0.009 | 0.029 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 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".