Psychometric evaluation of the Affect Regulation Checklist: Clinical and community samples, parent-reports and youth self-reports
Bibliographic record
Abstract
The Affect Regulation Checklist (ARC) was designed to capture affect dysregulation, suppression, and reflection. Importantly, affect dysregulation has been established as a transdiagnostic mechanism underpinning many forms of psychopathology. We tested the ARC psychometric properties across clinical and community samples and through both parent-report and youth self-report information. Clinical sample: participants included parents (n=814; Mage=43.86) and their child (n=608; Mage=13.98). Community sample: participants included independent samples of parents (n=578; Mage=45.12) and youth (n=809; Mage=15.67). Exploratory structural equation modeling supported a three-factor structure across samples and informants. Dysregulation was positively associated with all forms of psychopathology. In general, suppression was positively associated with many forms of psychopathology, and reflection was negatively associated with externalizing problems and positively associated with internalizing problems.
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 | 0.012 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".