Adaptation and Validation of the Bullying and Cyberbullying Scale for Adolescents in Bangla
Bibliographic record
Abstract
INTRODUCTION: Adolescent bullying is a pressing global public health and educational issue, including in Bangladesh. The lack of valid, reliable assessment tools in clinical and school settings impedes the identification of victims and individuals involved in bullying and cyberbullying. This study aimed to adapt and validate the Bullying and Cyberbullying Scale for Adolescents (BCS-A) in Bangla. METHODS: This cross-sectional study followed a standard adaptation protocol to translate the BCS-A into Bangla. The study included 210 randomly selected adolescents (12-17 years) from three Bangla-medium schools in Dhaka. Participants completed the Bangla-adapted BCS-A, a sociodemographic questionnaire, and the Strengths and Difficulties Questionnaire (SDQ). RESULTS: The mean score of the victimization subscale was 9.60 (SD: ±9.1) and the perpetration subscale was 5.15 (SD: ±6.55). The Cronbach's alpha values were 0.821 and 0.808 for the victimization and perpetration subscales, respectively, indicating robust reliability. For the majority of items, the item-total correlation was above 0.30. Confirmatory factor analysis indicated a four-factor structure for each of the subscales, aligning with previous studies. Victims of bullying showed a positive correlation with internalizing problems on the SDQ, whereas perpetrators of bullying showed a positive correlation with externalizing problems, suggesting good convergent and divergent validity. A total victimization score of ≥ 5 and a total perpetration score of ≥ 4 demonstrated the ability to accurately identify victims and perpetrators, respectively, with over 80% sensitivity and specificity. CONCLUSION: The BCS-A Bangla has demonstrated strong reliability and validity, making it a useful tool for screening bullying and cyberbullying behaviors among adolescents.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".