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Record W4283375195 · doi:10.2196/40022

Posttraining Outcomes, Acceptability, and Technology-Based Delivery of the STAC Bystander Bullying Intervention Teacher Module: Mixed Methods Study

2022· article· en· W4283375195 on OpenAlexvenueno aff
Aida Midgett, Diana M. Doumas, Mary Klein Buller

Bibliographic record

VenueJMIR Formative Research · 2022
Typearticle
Languageen
FieldPsychology
TopicBullying, Victimization, and Aggression
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyIntervention (counseling)Focus groupApplied psychologyMedical educationMedicineBusiness

Abstract

fetched live from OpenAlex

BACKGROUND: Bullying is a significant problem for youth associated with wide-ranging negative consequences. Providing students who witness bullying with intervention strategies to act as defenders can reduce bullying and negative associated outcomes for both targets and bystanders. Educating teachers about bullying and training them to support students to intervene as defenders may increase the efficacy of bystander programs as teachers' attitudes and responses to bullying relate to bystander behavior. This is particularly important in middle school, when bullying peaks and rates of reporting bullying to teachers begin to decline. Reducing implementation barriers, including limited time and resources, must also be considered, particularly for schools in low-income and rural areas. Technology-based programs can increase access and scalability but require participant buy-in for adoption. OBJECTIVE: We used a mixed methods design to inform the development of the STAC teacher module, a companion training to a brief bullying bystander intervention. STAC stands for the four bystander intervention strategies: Stealing the Show, Turning it Over, Accompanying Others, and Coaching Compassion. Objectives included examining the effectiveness of the STAC teacher module and informing the translation of the training into a technology-based format that can be used as a companion to the technology-based STAC. METHODS: A sample of 17 teachers recruited from 1 middle school in a rural, low-income community completed pre- and posttraining surveys assessing immediate outcomes (ie, knowledge, confidence, comfort, and self-efficacy), intention to use program strategies, and program acceptability and relevance, followed by a qualitative focus group obtaining feedback regarding program appropriateness, feasibility, content, perception of need, and desire for web-based training. Descriptive statistics, 2-tailed independent-sample t tests, and thematic analyses were used to analyze the data. RESULTS: Assessment of pre- and posttraining surveys indicated that teachers reported an increase in knowledge and confidence to support defenders, confidence and comfort in managing bullying, and bullying self-efficacy. Furthermore, most participants reported that they were likely or very likely to use STAC strategies to support students who intervene in bullying. Quantitative and qualitative data revealed that participants found the training easy to use, useful, relevant, and appropriate. Qualitative data provided feedback on ways of improving the program, including revising role-plays and guidance on understanding student behavior. Participants shared positive perceptions regarding program feasibility and need for bullying-specific prevention, the most significant barriers being cost and parent buy-in, suggesting the importance of including parents in the prevention process. Finally, participants shared the strengths of a web-based program, including ease of implementation and time efficiency, while indicating the importance of participant engagement and administration buy-in. CONCLUSIONS: This study demonstrates the effectiveness of the STAC teacher module in increasing knowledge and bullying self-efficacy and provides support for developing the module, including key information regarding considerations for web-based translation.

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)0.006
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.230
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.082
GPT teacher head0.472
Teacher spread0.390 · 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.

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

Citations11
Published2022
Admission routes1
Has abstractyes

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