Preregistered Direct Replication and Extension of “The Wisdom to Know the Difference: Strategy-Situation Fit in Emotion Regulation in Daily Life Is Associated With Well-Being”
Bibliographic record
Abstract
Certain emotion-regulation strategies (e.g., reappraisal) are associated with better well-being and are therefore seen as adaptive (health-promoting) strategies. However, it is unlikely that any strategy is adaptive regardless of context. Indeed, reappraisal is associated with positive outcomes in the context of uncontrollable life stress but negative outcomes in the context of controllable life stress. It follows that individuals who have better “strategy-situation fit” (use reappraisal more during uncontrollable vs. controllable situations) should have better well-being beyond their habitual reappraisal use. A previous test of this hypothesis found that strategy-situation fit in daily life was associated with greater well-being ( N = 74). We conducted a well-powered preregistered direct replication of this study in 285 U.S. adults. We failed to replicate the original findings and found no evidence for the strategy-situation fit hypothesis, including when accounting for key confounders and moderators. We discuss implications for theory and future research.
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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 | 0.014 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".