Believing is doing: Emotion regulation beliefs are associated with emotion regulation behavioral choices and subjective well-being
Bibliographic record
Abstract
Research in emotion regulation has begun to examine various predictors of emotion regulation choices, including individual differences and contextual variables. However, scant attention has been paid to the extent to which people's beliefs about the specific consequences of emotion regulation strategies for the components of an emotional response and long-term well-being predict their behavioral regulatory choices and, in turn, their subjective well-being. Participants completed measures to assess their beliefs about the consequences of functional and dysfunctional strategies, behavioral choices of emotion regulation strategies in negative scenarios, and subjective well-being. The model that fit the data indicated partial mediation whereby beliefs were associated with approximately 9% of the variance in choices. Emotion regulation choices were related to subjective well-being, with an additional direct effect between beliefs and well-being. This suggests beliefs play a role in people's regulatory choices. Future research should explore how beliefs interact with individual differences and contextual variables to better understand why people regulate their emotions in different ways and, ultimately, to help individuals make healthy emotion regulation choices.
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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 | 0.008 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".