Measuring Subtypes of Emotion Regulation: From Broad Behavioural Skills to Idiosyncratic Meaning‐making
Bibliographic record
Abstract
UNLABELLED: The current paper introduces the notion of clinically relevant subtypes of emotion regulation behaviours. A new measure of emotion regulation, the Complexity of Emotional Regulation Scale (CERS), was established as psychometrically sound. It was positively correlated with a measure of emotional awareness (r = 0.28, p < 0.001) and negatively correlated with measures of self-criticism (r = -0.28, p < 0.001) and depression (r = -0.35, p = 0.025), among others. Participants were drawn from two samples: clients from a university counselling centre and a non-clinical student sample. Comparisons were conducted between non-clinical and clinical samples to determine the effects of depression and other symptoms of psychopathology on participant's generation of strategies for emotion regulation. Participants in the clinical sample more often identified an intention to soothe but did not follow through as compared with the non-clinical group, F(1, 198) = 4.662, p < 0.04. Furthermore, individuals in the non-clinical sample were more likely to engage in specific, meaning-making strategies when compared with the clinical group, F(1, 198) = 5.875, p < 0.02. Implications from the current studies suggest the possible applicability of the CERS to clinical settings using an interview rather than questionnaire format. Copyright © 2015 John Wiley & Sons, Ltd. KEY PRACTITIONER MESSAGE: Emotion regulation should be thought of as being on a continuum of complexity, where strategies range from general ('one size fits all') action to specific ('personal and idiosyncratic') meaning. The best emotion regulation strategy depends on a client's presenting difficulty and level of distress.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".