Bullying Including Cyber Bullying Increases the Risk of Suicidal Behaviour
Bibliographic record
Abstract
Suicidal behaviour is one of the most common reasons for presentation to the emergency rooms. Bullying is a universal public health concern that affects significant number of adolescents. Many children and adolescents are recurrently involved in school bullying. Research suggests that both bullies and victims are overrepresented amongst those seen by mental health professionals. Understand the the relationship between bullying and suicidal behaviour, prevalence of different kinds of bullying in patients with mental health problems and prevalence of cyber bullying and it's affect on the victim Increase public awareness on importance of cyber bullying. We feel that many patients won't disclose that they had been or are being cyber bullied because the characteristics are unclear. Charts of all patients who visited emergency room from 2011 to 2013 with a mental health complaint were reviewed. Variables understudy were gender, history of bullying, type of bullying (verbal, physical, emotional), DSM-IV-TR diagnosis and outcome following the assessment. Our study shows significant association between bullying, and suicidal behaviours, although based on our study, this predictor was not commonly assessed . Our study showed that there was a significant link between bullying and future suicidal behaviour which is not commonly assessed. It is important that physicians identify these risk factor while assessing suicidality. Involvement in current cyber bullying was found to be less frequent than other forms of bullying such as verbal and physical. However, significant links were observed between cyber bullying and suicidal behaviour.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".