Cyberbullying in those at clinical high risk for psychosis
Bibliographic record
Abstract
AIM: Several studies suggest an association between experiences of childhood trauma including bullying and the development of psychotic symptoms. The use of communications technology has created a new media for bullying called 'cyberbullying'. Research has demonstrated associations between traditional bullying and cyberbullying. Negative effects of cyberbullying appear similar in nature and severity to the reported effects of traditional bullying. Our aim was to examine the prevalence and correlates of cyberbullying in those at clinical high risk (CHR) for psychosis. METHODS: Fifty young people at CHR for psychosis were administered the Childhood Trauma Questionnaire with added questions about cyberbullying. RESULTS: Cyberbullying was reported in 38% of the sample. Those who experienced cyberbullying also reported experiencing previous trauma. CONCLUSION: It is possible that cyberbullying may be a problem for those at CHR of psychosis, and due to the vulnerable nature of these young people may have longitudinal implications.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".