Suicide-related Internet use among university students
Bibliographic record
Abstract
Introduction Nowadays, mobile and Internet communication is widely used and has a special role in mental health prevention. Besides, websites targeting suicide prevention, pro-suicide contents (methods for suicide, suicide pacts) are also easily available, which may increase the risk for suicide in vulnerable people. Aims Our aim was to assess the relation between Internet use and suicidal behaviour among university students and also to assess online activity regarding suicidal contents and help-seeking behaviour. Methods Self-administered questionnaires were completed by university students. Results Most of the 101 students who completed the survey use the Internet 3 hours or more a day. They are facing suicidal contents numerous times. Professional websites providing information and the common popular sites were mainly visited, sites providing help were less screened (10%). More than quarter of the students felt discomfort when looking at sites dealing with suicide. Almost one-third of the subjects had suicidal thoughts during their lives and 15% already planned suicide. In case of suicidal thoughts, subjects would seek help mainly from friends and family, but online help-seeking was not preferred. Conclusions Despite of the extensive Internet use, students rarely seek help for emotional problems on the Internet. Development of websites controlled by professionals is essential, especially for those who would not benefit from traditional psychological/psychiatric care. Future research is needed regarding the characteristics of Internet use and the potentials and limits of help-seeking via the Internet in order to prevent people from pro-suicide websites and to improve professional websites. Disclosure of interest The authors have not supplied their declaration of competing interest.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".