Brief report: A confirmatory factor analysis of the Child Behavior Checklist in a large sample of autistic youth
Bibliographic record
Abstract
Autistic youth often experience co-occurring psychiatric conditions. Checklist measures such as the Child Behavior Checklist (CBCL) can assist clinicians and researchers in assessing the symptom profiles of such conditions. Symptom profiles often overlap between autism and cooccurring psychiatric conditions (e.g., depression) in which the same symptoms occur in both. Previous research investigating the validity of the CBCL in autistic populations using factor structure has been mixed . Seven-hundred-and-fourteen autistic youth (293 females) aged 6–18 years (M = 11.25, SD = 3.29) participated. A confirmatory factor analysis of the 8-factor CBCL-6–18 was completed. Results suggest a poor model fit in autistic samples of the widely used eight-scale factor structure. This model may not fit this sample due to the overlap of symptomatology autism has with other psychiatric condition profiles (e.g., communication and behaviors). Future research and implications, including an exploratory factor analysis on the CBCL/6–18 for autistic populations, are discussed. • The CBCL is commonly used to assess co-occurring conditions in autism. • We conducted an examination of the 8-factor factor structure. • The eight-factor factor structure was found to be a poor fit. • Several items did not load onto the eight-factor structure. • The eight-factor structure of the CBCL may not be appropriate for assessing co-occurring conditions in autism.
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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 | 0.016 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".