Cyberbullying in Children and Youth: Implications for Health and Clinical Practice
Bibliographic record
Abstract
We review the recent literature on cyberbullying and its effects on victimised youth, identifying key points. We conclude that cyberbullying, while following many of the underlying dynamics of more traditional forms of bullying, features some unique qualities that can both magnify the damage caused and make it more difficult to detect. These features include the pervasive, never-ending nature of cyberbullying and the ability to quickly reach large audiences. The potential for anonymity and the related distance afforded by screens and devices compared to in-person interaction allow the cruelty of cyberbullying to go unchecked. Despite the perceived anonymity of cyberbullying, cyberbullying can be perpetrated by friends, who often have intimate knowledge about the victimised youth that can be devastating when made public. Given the difficulty schools face in preventing or even detecting cyberbullying, health care providers are an important ally, due to their knowledge of the youth, the sense of trust they bring to youth, and their independence from the school setting. We conclude by calling for routine screening of bullying by health care providers who deal with paediatric populations.
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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".