Emotion regulation in action: Use, selection, and success of emotion regulation in adolescents’ daily lives
Bibliographic record
Abstract
Successful emotion regulation (ER) is a central aspect of psychosocial functioning and mental health and is thought to improve and be refined in adolescence. Past research on ER has mainly focused on one-time measurements of habitual ER. Linking regulatory strategies to emotions in daily lives is key to understanding adolescents’ emotional lives. Using an Experience Sampling Method with 78 adolescents ( M age = 13.91, SD age = .95, 66% girls), we investigated the use, selection, and success in down-regulating negative emotions of eight ER strategies across 44 assessments. Acceptance was the strategy employed most often followed by problem-solving, rumination, distraction, avoidance, reappraisal, social support, and suppression. Interestingly, negativity of the event influenced the use of ER strategies: With low intensity negative emotions, acceptance was more likely to be used, and with high intensity negative emotions, suppression, problem-solving, distraction, avoidance, social support, and rumination were more likely to be used. With regard to success, multilevel models revealed that problem-solving, reappraisal, and acceptance were more successful in down-regulating negative emotions than rumination. Further, among girls, no relations between the momentary use of ER strategies and depressive symptoms was found. Among boys, a negative relation between acceptance and depressive symptoms emerged. Results from this study suggest that there is a reciprocal relationship between the intensity of negative emotions and ER strategies and that gender differences may exist. Taken together, this study showed which ER strategies are used by a healthy adolescent sample, and these results are discussed with regard to their theoretical and practical importance.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".