Validation of a cross-cultural instrument for child behavior problems: the Disruptive Behavior International Scale – Nepal version
Bibliographic record
Abstract
BACKGROUND: Obtaining accurate and valid measurements of disruptive behavior disorders remains a challenge in non-Western settings due to variability in societal norms for child behavior and a lack of tools developed outside of Western contexts. This paper assesses the reliability and construct validity of the Disruptive Behavior International Scale - Nepal version (DBIS-N)-a scale developed using ethnographic research in Nepal-and compares it with a widely used Western-derived scale in assessing locally defined child behavior problems. METHODS: We assessed a population-based sample of 268 children ages 5-15 years old in Nepal for behavior problems with a pool of candidate items developed from ethnographic research. We selected final items for the DBIS-N using exploratory factor analysis in a randomly selected half of the sample and then evaluated the model fit using confirmatory factor analysis in the remaining half. We compared the classification accuracy and incremental validity of the DBIS-N and Eyberg Child Behavior Inventory (ECBI) using local defined behavior problems as criteria. Local criteria were assessed via parent report using: 1) local behavior problem terms, and 2) a locally developed vignette-based assessment. RESULTS: Ten items were selected for the final scale. The DBIS-N had good internal consistency (Cronbach's α: 0.84) and excellent test-retest reliability (intraclass correlation 0.93, r = .93). Classification accuracy and area under the curve (AUC) were similar and high for both the ECBI (AUC: 0.83 and 0.85) and DBIS-N (AUC: 0.83 and 0.85) on both local criteria. The DBIS-N added predictive value above the ECBI in logistic regression models, supporting its incremental validity. CONCLUSIONS: While both the DBIS-N and the ECBI had high classification accuracy for local idioms for behavior problems, the DBIS-N had a more coherent factor structure and added predictive value above the ECBI. Items from the DBIS-N were more consistent with cultural themes identified in qualitative research, whereas multiple items in the ECBI that did not fit with these themes performed poorly in factor analysis. In conjunction with practical considerations such as price and scale length, our results lend support for the utility of the DBIS-N for the assessment of locally prioritized behavior problems in Nepal.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".