Instruments to Gauge Emotional Regulation in Children with Disabilities: A Scoping Review
Bibliographic record
Abstract
Background: Children with disabilities have less emotional regulation than children without disabilities, and 62.2% have mental health disorders, leading to high levels of comorbidity. Various instruments to measure emotion regulation in children have been developed, but instruments with adequate validity and reliability have not been reported. One way to gauge emotional regulation is to use a measurement instrument. This study aimed to explore instruments of assessment of emotion regulation in children with disabilities that have adequate validity and reliability. Methods: This scoping review was conducted to explore the instruments measuring the emotional regulation of children with disabilities. The search was conducted through the Google Scholar, PubMed, and Science Direct databases and included articles published between 2016-2021. The selection process was done according to the Preferred Reporting Elements for Systematic Review and Descriptive Analysis (PRISMA) guidelines. The search process used appropriate populations, concepts, and contexts. The critical appraisal used the Joanna Briggs Institute checklist. Results: Out of 22,835 articles, 14 articles were selected for this review. Some instruments can be applied to measure the emotional regulation of children with disabilities, such as ERC, DERS, FEEL-KJ, ERICA, EDI, BRIEF, ERQ, and CERQ. In general, if the instrument has a good internal consistency, then it can be used to measure the emotional regulation of children and teenagers with disabilities aged 4-19 years. Conclusion: All of the identified instruments can be used to measure the emotional regulation of children with disabilities.
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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 | 0.023 | 0.106 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.025 | 0.021 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".