Deriving an Adolescent Executive Behavior Screener from the Behavior Assessment System for Children—2
Bibliographic record
Abstract
OBJECTIVE: Typical executive functioning (EF) measurements do not reflect the complexity of daily life. We derived an executive behavior screener from the BASC-2-PRS-A using a previously derived four-component model of EF and provided support for the use of the screener in adolescent populations. METHODS: A total of 2,722 census-matched American adolescents were sampled. We assigned 25 items a priori to four executive factors (problem solving, attentional control, behavioral control, and emotional control) and evaluated via confirmatory factor analysis, invariance testing and differential item functioning (DIF) models. RESULTS: We found acceptable-to-good reliability and that the four-factor model had the best fit. We showed DIF for age and socioeconomic status (SES). While groups were invariant based on sex, latent mean comparisons showed significant differences. CONCLUSIONS: Construct validity of the adolescent four-factor model as measured through the screener was supported. Females demonstrated fewer executive behavior problems. Standardized norms are available and split by age and sex. SES may influence the interpretation of T-scores. Continued exploration and development of the screener is suggested.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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 | 0.001 |
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".