What Influences Educators’ Design Preferences for Bullying Prevention Programs? Multi-level Latent Class Analysis of a Discrete Choice Experiment
Bibliographic record
Abstract
We used a discrete choice conjoint experiment to model the anti-bullying (AB) program preferences of 1080 junior kindergarten to Grade 8 educators. Participants chose between hypothetical AB programs that varied combinations of 12 design attributes. Multi-level latent class analysis yielded three classes: All - in Supervisors (21.5%) preferred that all teaching staff supervise playgrounds and hallways; Facilitators (61.6%) preferred that students take ownership of AB activities with 25% of educators supervising playgrounds and hallways; and Reluctant Delegators (16.9%) preferred delegating the supervision of playgrounds and hallways to non-teaching staff. This class reported higher dispositional reactance, more implementation barriers, and more psychological reactance to these initiatives. They were less sensitive to social influences and less intent on participating in AB activities. Multi-level analysis showed a greater proportion of Reluctant Delegators clustered in one of the two groups of schools. The program choices of all classes were sensitive to the support of principals, colleagues, students, and, to a lesser extent, parents. All classes preferred programs conducted from kindergarten through Grade 12 that addressed the problems underlying bullying while valuing firm and consistent consequences for all students. Educators preferred AB programs selected by individual schools, rather than governments.
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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 | 0.021 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".