Cross-cultural validation of a behavioral screener for executive functions: Guidelines for clinical use among Colombian children with and without ADHD.
Bibliographic record
Abstract
Garcia-Barrera, Kamphaus, and Bandalos (2011) derived a 25-item executive functioning screener from the Behavior Assessment System for Children (BASC), measuring 4 latent executive constructs: problem solving, attentional control, behavioral control, and emotional control. The current study included a cross-cultural examination of this screener in Colombian children with and without attention-deficit/hyperactivity disorder (ADHD). BASC teacher ratings were collected for Colombian children ages 6-11 years (848 healthy children [53% boys] and 155 children with ADHD [76% boys]). To examine the psychometric properties of the screener, a multistep procedure was implemented, including (a) confirmatory factor analysis (CFA) and factorial invariance testing across gender, age group (6-8 years, 9-11 years), and ADHD status to replicate and extend the original derivation; (b) item response theory (IRT) analysis to evaluate the information provided by individual items; and (c) given IRT results, a repeated CFA and invariance testing after the exclusion of 1 item from the problem-solving factor. The 24-item 4-factor model fit was adequate for controls and for ADHD participants. Results support the use of the 24-item executive functioning screener in a cross-cultural context. In turn, in supplemental material, normative data for the Colombian sample are reported along with bilingual guidelines (i.e., Spanish/English) for implementing the screener in clinical practice. Even though the screener is useful when examining executive functions, it was not designed as a diagnostic measure for developmental disorders such as ADHD; as such, it should only inform about status of executive functioning.
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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 | 0.010 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".