Recommendations for cyberbullying prevention and intervention: A Western Canadian perspective from key stakeholders
Bibliographic record
Abstract
Introduction: Cyberbullying, or repeatedly communicating antagonistic messages using digital or electronic media meant to deal out harm or discomfort to others, has been considered more pervasive and impactful than traditional bullying since perpetrators can remain anonymous online, are not bound by time or place. In addition, cyberbullied youth are reluctant to involve others such as an adult or confront the perpetrator adults. Therefore, the primary purpose of this study was to capture a holistic understanding of potential youth cyberbullying prevention and intervention strategies (i.e., inhibiting forces that may reduce cyberbullying) from key stakeholders with professional knowledge about cyberbullying (i.e., educational administration, psychological counseling, technology and bullying education consultation, policing, research, and social support services). Methods: Model, a process-oriented metatheory of aggression with the potential to explain how cyberbullying behaviors continue to occur, was used as a frame to analyze the qualitatively gathered data using six phases of reflexive thematic analysis. Results: Participants identified educational efforts related to awareness of cyberbullying and consequences of perpetration, digital citizenship programming for students and social skills training, providing remediation to youth who are in online conflict with one another, and parental engagement with the technology used by their youth as key factors in mitigating instances of cyberbullying. Discussion: This study furthers research on cyberbullying prevention and intervention in schools by illuminating experiences from under researched and unique stakeholders in the field. These key findings and suggestions for future research are further discussed.
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 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 | 0.016 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.027 | 0.011 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.007 | 0.008 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
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".