Psychological Impact of Cyber-Bullying: Implications for School Counsellors L’effet psychologique de cyber-intimidation: Implications pour les conseillers scolaires
Bibliographic record
Abstract
Cyber-bullying is a significant problem for children today. This study provides evidence of the psychological impact of cyber-bullying among victimized children ages 10 to 17 years (M = 12.48, SD = 1.79) from 23 urban schools in a western province of Canada (N = 239). Students who were cyber-bullied reported high levels of anxious, externalizing, and depressed feelings/behaviours for all types of cyber-bullying they experienced, with girls reporting more severe impact than boys. Strategies are discussed for school counsellors working with youth who have been victimized through electronic means. résumé Cyber-intimidation est un problème important pour les enfants d’aujourd’hui. Cette étude fournit des preuves de l’effet psychologique de la cyber-intimidation chez les victimes, des enfants âgés de 10 à 17 ans (M = 12,48, SD = 1,79) de 23 écoles urbaines dans une province de l’ouest du Canada (N = 239). Les étudiants, victimes de cyber-intimidation, ont déclaré avoir vécu des sentiments et des comportements d’anxiété, d’extériorisation, et de dépression pour tous les types de cyber-intimidation qu’ils ont vécus. Les filles
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".