Validation of the Moral Disengagement for Adolescent Dating Violence Prevention Scale With Teacher Trainees
Bibliographic record
Abstract
Once in the workforce, teachers are often asked to participate in school-based adolescent dating violence prevention efforts. However, our understanding of how willing and able future teachers are to engage in dating violence prevention is limited. This may be due, in part, to the lack of available measurement tools. Understanding willingness before teachers are in the classroom is key to exploring how to help future teachers be more ready and able to engage in prevention efforts once they are in the classroom. Thus, the purpose of the current study was to develop and test a measure that assesses one aspect of teacher trainees’ willingness to engage in dating violence prevention efforts: moral disengagement. Using two independent samples of teacher trainees ( N = 400; 64.5% White, 75.0% female, 84.5% heterosexual), we explored the factor structure of the Moral Disengagement for Adolescent Dating Violence Prevention (MD-ADVP) scale. We conducted exploratory factor analysis (Sample 1, n = 222) and confirmatory factor analysis (Sample 2, n = 178), and also examined the factor structure across sub-groups and assessed internal consistency reliability and construct validity evidence. Analyses suggest the MD-ADVP is unidimensional, and that this factor structure holds across sub-groups. We found strong evidence of both reliability and construct (convergent and divergent) validity. As hypothesized, scores on the MD-ADVP demonstrated significant negative bivariate associations with scores on three measures of adolescent dating violence prevention-related beliefs, and no association with scores on a measure of weight bias. The MD-ADVP will advance research investigating teacher preparation for adolescent dating violence prevention efforts. For example, use of the MD-ADVP can illuminate whether teacher trainees’ moral disengagement is an indicator of future implementation success. Further testing of this measure in racially and gender diverse samples is needed.
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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 | 0.009 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| 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 | 0.001 | 0.002 |
| 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".