The interpersonal risks of valuing happiness: Links to interpersonal emotion regulation and close others’ mental health.
Bibliographic record
Abstract
= 279, including partially overlapping groups of 155 parents and 248 partnered individuals) and cross-sectional reports from partners. We found that people who aspired to be happy were more successful at using reappraisal and distraction to manage targets' emotions, while those who were concerned about happiness were less successful at accepting targets' emotions (i.e., confirmed by partners' reports). In turn, more successful use of reappraisal and distraction predicted better target well-being, and less successful acceptance of targets' emotions predicted poorer target well-being across the next 8 months. These findings underscore the importance of understanding individual differences that shape consequential forms of interpersonal emotion regulation, thereby illuminating who is most likely to help their loved ones and who may be putting them at risk. (PsycInfo Database Record (c) 2025 APA, all rights reserved).
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".