Is the negative always that bad? Or how emotion regulation and integration of negative memories can positively affect well‐being
Bibliographic record
Abstract
OBJECTIVE: The purpose of this study was to determine whether coherent integration of negative memories into the self could positively predict well-being over time, and whether certain emotion regulation strategies could facilitate this coherent integration. In turn, coherent integration of negative memories was expected to further facilitate adaptive emotion regulation strategies over time. METHOD: A total of 303 participants took part in this longitudinal study. At Phase 1, they completed measures of emotion regulation and well-being. Three months later, they described the memory of the most negative event they experienced since Phase 1, and completed measures assessing its integration. One month later, participants completed the well-being measures again, and another month later, their emotion regulation was reassessed. RESULTS: Adaptive emotion regulation predicted adaptive memory integration, which in turn led to increases in well-being and adaptive emotion regulation. Contrariwise, the incapacity to adaptively regulate emotions predicted poor memory integration, which in turn led to decreases in well-being. CONCLUSION: The way people regulate their negative emotions acts as an individual difference influencing how negative memories are integrated into the self, which can in return alter well-being and emotion regulation capacity over time.
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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 | 0.003 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".