Behavioural addictions as risk factors for incidence and reoccurrence of suicide ideation and attempts in a prospective cohort study among young swiss men
Bibliographic record
Abstract
Introduction Substance use disorder, depression and sexual minority are well documented risk factors for suicidal behaviour, far less is known about behavioural addictions. Objectives First, to explore associations between behavioural addictions (gaming, gambling, cybersex, internet, smartphone, work) at age 25 and the incidence and reoccurrence of suicide ideation (SID), suicide attempts (SAT), and suicide attempts among those with suicide ideation (SATID) at age 28. Second, to test whether these associations were impacted by adjusting for cannabis and alcohol use disorder, nicotine dependence, sexual orientation and depression. Methods Based on two waves of a prospective cohort study of 5,428 young Swiss men, nested models with and without adjustment for risk factors were used to regress SID, SAT and SATID on preceding behavioural addictions. Results Without adjustment, each of the behavioural addictions at age 25 significantly predicted the incidence of SID and SAT at age 28. Gambling and cybersex addiction furthermore predicted SATID. When adjusting for other risk factors, associations with behavioural addictions were reduced, whereas depression and cannabis use disorder were the most important and consistent predictors for the incidence and recurrence of SID, SAT and SATID. Conclusions Among young Swiss men, behavioural addictions are important predictors of SID and SAT, however a large part of their association is shared with depression and cannabis use disorder. Treatment for addictive behaviors, especially cannabis use can open the door to larger mental health screening and targeted intervention. Crisis intervention among men presenting addictive behaviours with or without substance may therefore be key to preventing suicidal behaviour. Disclosure No significant relationships.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".