Regulating Emotion Systems in Everyday Life
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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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 | 0.003 | 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 | 0.001 |
| 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 it