Stepwise Development of a Text Messaging-Based Bullying Prevention Program for Middle School Students (BullyDown)
Bibliographic record
Abstract
BACKGROUND: Bullying is a significant public health issue among middle school-aged youth. Current prevention programs have only a moderate impact. Cell phone text messaging technology (mHealth) can potentially overcome existing challenges, particularly those that are structural (e.g., limited time that teachers can devote to non-educational topics). To date, the description of the development of empirically-based mHealth-delivered bullying prevention programs are lacking in the literature. OBJECTIVE: To describe the development of BullyDown, a text messaging-based bullying prevention program for middle school students, guided by the Social-Emotional Learning model. METHODS: We implemented five activities over a 12-month period: (1) national focus groups (n=37 youth) to gather acceptability of program components; (2) development of content; (3) a national Content Advisory Team (n=9 youth) to confirm content tone; and (4) an internal team test of software functionality followed by a beta test (n=22 youth) to confirm the enrollment protocol and the feasibility and acceptability of the program. RESULTS: Recruitment experiences suggested that Facebook advertising was less efficient than using a recruitment firm to recruit youth nationally, and recruiting within schools for the pilot test was feasible. Feedback from the Content Advisory Team suggests a preference for 2-4 brief text messages per day. Beta test findings suggest that BullyDown is both feasible and acceptable: 100% of youth completed the follow-up survey, 86% of whom liked the program. CONCLUSIONS: Text messaging appears to be a feasible and acceptable delivery method for bullying prevention programming delivered to middle school students.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".