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Record W4220956173 · doi:10.1177/26320770211064330

The Changing World of Bullying: Student Strategies for Cyberbullying Intervention

2022· article· en· W4220956173 on OpenAlexaff
Courtney Andrysiak, Priya S. Mani, Marlene Pomrenke, Grace Ukasoanya, Lauren Mizock

Bibliographic record

VenueJournal of Prevention and Health Promotion · 2022
Typearticle
Languageen
FieldPsychology
TopicBullying, Victimization, and Aggression
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsCoping (psychology)PsychologyCognitive reframingPsychological interventionSocial psychologyApplied psychologyClinical psychology

Abstract

fetched live from OpenAlex

A gap in research on cyberbullying intervention strategies exists. The purpose of this study was to identify effective coping strategies for cyberbullying by interviewing cyberbullying survivors. The study used grounded theory qualitative methodology to allow data to fully emerge from participants’ perspectives. When analyzing the data, the researchers found that youth engaged in three types of coping: online coping, offline coping, and intrinsic coping. Online coping involved online interventions to stop active cyberbullying and prevent future cyberbullying; for example, youth limited who had access to their online accounts or blocked and reported cyberbullies. Offline coping included strategies that participants engaged in offline to minimize, tolerate, or cope with the effects of cyberbullying, such as talking about their experiences or reframing the way that they think about things. Finally, intrinsic coping described survivors’ personality traits or ways of being that aided them in developing such resilient coping strategies; for instance, possessing self-awareness and self-love contributed to survival. Accordingly, the findings contribute to the literature on effective coping strategies by confirming previously identified strategies, like online coping, and highlighting new ones, like intrinsic coping. The findings also help inform future counseling practices within schools.

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)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.696
Threshold uncertainty score0.671

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.079
GPT teacher head0.428
Teacher spread0.349 · 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 designOther design
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

Citations2
Published2022
Admission routes1
Has abstractyes

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