A Systematic Literature Review of Emotion Regulation Measurement in Individuals With Autism Spectrum Disorder
Bibliographic record
Abstract
Emotion regulation (ER) difficulties are a potential common factor underlying the presentation of multiple emotional and behavioral problems in individuals with Autism Spectrum Disorder (ASD). To provide an overview of how ER has been studied in individuals with ASD, we conducted a systematic review of the past 20 years of ER research in the ASD population, using established keywords from the most comprehensive ER literature review of the typically developing population to date. Out of an initial sampling of 305 studies, 32 were eligible for review. We examined the types of methods (self-report, informant report, naturalistic observation/ behavior coding, physiological, and open-ended) and the ER constructs based on Gross and Thompson's modal model (situation selection, situation modification, attention deployment, cognitive change, and response modulation). Studies most often assessed ER using one type of method and from a unidimensional perspective. Across the 32 studies, we documented the types of measures used and found that 38% of studies used self-report, 44% included an informant report measure, 31% included at least one naturalistic observation/behavior coding measure, 13% included at least one physiological measure, and 13% included at least one open-ended measure. Only 25% of studies used more than one method of measurement. The findings of the current review provide the field with an in-depth analysis of various ER measures and how each measure taps into an ER framework. Future research can use this model to examine ER in a multicomponent way and through multiple methods.
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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 | 0.012 | 0.069 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.020 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".