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Record W4406568336 · doi:10.2196/67962

Usability Testing of a Bystander Bullying Intervention for Rural Middle Schools: Mixed Methods Study

2025· article· en· W4406568336 on OpenAlexvenueno aff
Aida Midgett, Diana M. Doumas, Carlos Peralta, Matt Peck, Blaine Reilly, Mary Klein Buller

Bibliographic record

VenueJMIR Human Factors · 2025
Typearticle
Languageen
FieldPsychology
TopicBullying, Victimization, and Aggression
Canadian institutionsnot available
FundersNational Institute on Minority Health and Health Disparities
KeywordsUsabilityPreprintBystander effectIntervention (counseling)PsychologyComputer scienceApplied psychologyWorld Wide WebSocial psychologyHuman–computer interaction

Abstract

fetched live from OpenAlex

BACKGROUND: Targets of bullying are at high risk of negative socioemotional outcomes. Bullying programming in rural schools is important as bullying is more prevalent in those schools compared to urban schools. Comprehensive, school-wide bullying programs require resources that create significant barriers to implementation for rural schools. Because technology-based programs can reduce implementation barriers, the development of a technology-based program increases access to bullying prevention in rural settings. OBJECTIVE: We aimed to conduct usability testing of a bystander bullying intervention (STAC-T). We assessed usability and acceptability of the STAC-T application and differences in usability between school personnel and students. We were also interested in qualitative feedback about usability, program features, and feasibility. METHODS: A sample of 21 participants (n=10, 48% school personnel; n=11, 52% students) recruited from 2 rural middle schools in 2 states completed usability testing and a qualitative interview. We used descriptive statistics and 2-tailed independent-sample t tests to assess usability and program satisfaction. We used consensual qualitative research as a framework to extract themes about usefulness, relevance, needs, barriers, and feedback for intervention development. RESULTS: Usability testing indicated that the application was easy to use, acceptable, and feasible. School personnel (mean score 96.0, SD 3.9) and students (mean score 88.6, SD 9.5) rated the application well above the standard cutoff score for above-average usability (68.0). School personnel (mean score 6.10, SD 0.32) and students (mean score 6.09, SD 0.30) gave the application high user-friendliness ratings (0-7 scale; 7 indicates highest user-friendliness). All 10 school personnel stated they would recommend the program to others, and 90% (9/10) rated the program with 4 or 5 stars. Among students, 91% (10/11) stated they would recommend the program to others, and 100% (11/11) rated the program with 4 or 5 stars. There were no statistically significant differences in ratings between school personnel and students. Qualitative data revealed school personnel and students found the application useful, relevant, and appropriate while providing feedback about the importance of text narration and the need for teacher and parent training to accompany the student program. The data showed that school personnel and students found a tracker to report different types of bullying witnessed and strategies used to intervene by students a useful addition to STAC-T. School personnel reported perceiving the program to be practical and very likely to be adopted by schools, with time, cost, and accessibility being potential barriers. Overall, findings suggest that the STAC-T application has the potential to increase access to bullying prevention for students in rural communities. CONCLUSIONS: The results demonstrate high usability and acceptability of STAC-T and provide support for implementing a full-scale randomized controlled trial to test the efficacy of the application.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.050
Threshold uncertainty score0.688

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.100
GPT teacher head0.441
Teacher spread0.341 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations7
Published2025
Admission routes1
Has abstractyes

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