Understanding reappraisal as a multicomponent process: The psychological health benefits of attempting to use reappraisal depend on reappraisal success.
Bibliographic record
Abstract
When is reappraisal-reframing a situation's meaning to alter its emotional impact-associated with psychological health? To answer this question, we should consider that reappraisal is a multicomponent process that includes, first, deciding to attempt to use reappraisal and, second, implementing reappraisal with varying degrees of success. Although theories of emotion regulation suggest that both attempting reappraisal more frequently and implementing reappraisal more successfully are necessary to achieve greater psychological health, no research has directly tested this assumption. We propose that daily diaries are particularly well suited to assess these 2 components because diaries can capture repeated attempts and success in daily life and with relative precision. In a sample of community adults (N = 219), we found that among participants experiencing elevated life stress (but not among those experiencing lower life stress), attempting reappraisal more frequently was associated with fewer depressive symptoms for those who used reappraisal more successfully, but was associated with somewhat more depressive symptoms for those who used reappraisal less successfully. These findings suggest that attempting reappraisal is associated with benefits only when individuals can implement it successfully. Thus, to fully understand the health implications of emotion regulation, we must consider it as a multicomponent process. (PsycINFO Database Record
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".