Investigating the general psychopathology factor in autistic youth
Bibliographic record
Abstract
Autistic youth are at higher risk of presenting with co-occurring internalizing (I) (i.e., anxiety and depression) and externalizing (E) (i.e., aggression and impulsivity) disorders (Bauminger et al., 2010). The Child Behavior Checklist for ages 6–18 (CBCL/6-18; Achenbach & Rescorla, 2001) is a measure of I-E disorders and symptoms in autistic and neurotypical youth, providing norm-referenced subscales as factors for each form of psychopathology. The general psychopathology or “ p ” factor may provide a better measure of co-occurring disorders in autism as it has not been evaluated in this population contextually to date. The p factor proposes that psychopathological disorders come from the same etiological factor, implying that we can measure all I-E disorders as indicators of p . Using archival data from the Province of Ontario Neurodevelopmental Disorders (POND) Network , ( N = 782) autistic youths’ raw scores from the CBCL/6-18 were analyzed using two confirmatory factor analyses (CFAs): an I-E CFA and a p factor CFA. An exploratory factor analysis (EFA) was also conducted to determine the best-fitting factor structure. A chi-square difference test compared each CFA to find the best model fit. Results reported each model as individually significant, however, based on recommendations from Hoyle and Panter (1995), neither model had an acceptable fit. Given that neither the p factor nor the internalizing/externalizing factor models had appropriate fit, it is recommended that future research investigate whether the CBCL/6–18 is the most appropriate measure for assessing co-occurring symptoms in autistic youth. The results of the EFA also suggest that the CBCL may not be the most appropriate measure for autistic youth. • This is the first study to investigate the p factor for co-occurring conditions in autism. • We used CFAs to investigate fit in I-E or p- factor models. • Neither I-E nor p- factor models were a good fit to the data. • Several items had to be eliminated as they did not load on their respective factors. • The CBCL/6–18 needs further research to determine its utility in autism research.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".