Do motives matter? Short- and long-term motives as predictors of emotion regulation in everyday life.
Bibliographic record
Abstract
The ability to consider the future is critical to many human behaviors. Individuals who consider future outcomes of their actions are more likely to report using emotion regulation strategies that have enduring effects on feelings. However, there has been little examination of how variation in short- and long-term motives across events predicts emotion regulation strategy use. We examined the roles of both interindividual and intraindividual variation in short- and long-term motives in emotion regulation in daily life, while controlling for hedonic and instrumental motives. In a daily diary study (Study 1) and a mobile application study (Study 2), participants (N = 107 and N = 98) reported on their short- and long-term motives for regulation and their use of multiple emotion regulation strategies across multiple negative events. Across both studies, momentary long-term motives were predictive of several strategies, including problem-solving and reappraisal, both of which are associated with more positive mental health outcomes in the long-term. The results suggest that people's long-term motives vary across contexts and relate to the implementation of different regulatory strategies, and that these associations are at least partially independent of the role of hedonic and instrumental motives. (PsycInfo Database Record (c) 2022 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 | 0.000 | 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) | 0.003 | 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".