Confirmatory factor analysis of the Evidence-Based Practice Attitudes Scale with school-based behavioral health consultants
Bibliographic record
Abstract
BACKGROUND: The Evidence-Based Practice Attitude Scale (EBPAS) is a widely used tool, but it has not been adapted and validated for use in schools, the most common setting where youth access behavioral health services. This study examined the factor structure, psychometric properties, and criterion-related validity of the school-adapted EBPAS in a sample of school-based behavioral health consultants. METHOD: A research team comprised of experts in implementation of evidence-based practices in schools along with the original developer adapted the EBPAS for the school setting. The adapted instrument was administered to a representative sample (n = 196) of school-based behavioral health consultants to assess the reliability and structural validity via a series of confirmatory factor analyses. RESULTS: The original EBPAS factor structure was confirmed, with the final model supporting four first-order factors that load onto a second-order factor capturing general attitudes toward evidence-based practice. Correlations among the subscales indicated both unique and shared variance. Correlations between EBPAS scores and consultant variables demonstrated differential criterion-related validity, with the total score and the Requirements and Openness subscales demonstrating the strongest correlations. CONCLUSIONS: The adapted EBPAS performed well when administered to behavioral health consultants operating in the educator sector, supporting the relevance of assessing attitudes in school settings. Potential directions for future research and applications of the EBPAS in schools and other service sectors are discussed.
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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 | 0.025 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| 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) | 0.002 | 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".