Regulating Emotion Systems in Everyday Life
Bibliographic record
Abstract
Abstract. Researchers are increasingly using ecological momentary assessment (EMA) to investigate how people regulate their emotions from moment-to-moment in daily life. However, existing self-report measures of emotion regulation have been designed and validated to assess habitual/trait use of emotion regulation strategies and may therefore not be suited to assessing momentary emotion regulation. The present study aimed to develop a brief, yet reliable, EMA measure of emotion regulation in daily life by adapting the Regulation of Emotion Systems Survey (RESS; DeFrance & Hollenstein, 2017 ), a recently developed global self-report questionnaire assessing habitual use of six emotion regulation strategies. We created an EMA version of the RESS by selecting 12 items from the original scale and adapting them for EMA. We investigated the psychometric properties of the new RESS-EMA scale by administering it eight times daily for 7 days via smartphones to a sample of undergraduates ( n = 112). Results of multilevel modeling analyses supported the within- and between-person reliability and validity of the RESS-EMA scale and suggest that it is a viable way to comprehensively assess momentary emotion regulation strategy use in daily life.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| 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 | 0.001 |
| 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".