Careworkers’ Affect Regulation in Youth Residential Care: A Study on the Psychometric Properties of the Affect Regulation Checklist
Bibliographic record
Abstract
Abstract The ability of formal caregivers who work in residential care to regulate their emotions plays an important role in determining the quality of their care. However, there are few instruments to assess affect regulation in this context. This study addresses this gap by providing a preliminary analysis of the psychometric properties of the Affect Regulation Checklist (ARC) in a sample of Portuguese child careworkers in residential care settings. The ARC was administered to 212 careworkers working in 21 residential care institutions in the district of Porto/Portugal (M age = 40.99 years, SD = 11.05). Confirmatory factor analysis (CFA) and item response theory (IRT) analysis were used to examine the psychometric properties of ARC. CFA confirmed the three-factor solution proposed by the original authors (suppression; dysregulation; adaptive reflection) and provided evidence of the construct validity of the ARC. IRT analyses showed that all items were moderately to highly discriminant and that some items were more difficult than others. Support was found for the internal consistency and test-retest reliability of the ARC. Overall, the ARC is a psychometrically sound approach for assessing careworkers’ affect regulation strategies in the residential care context.
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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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 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".