Risk Factors for Suicide Ideation and Attempts Among Pathological Gamblers
Bibliographic record
Abstract
The link between pathological gambling and suicide is poorly understood. The current study has two major goals: to provide descriptive information about suicide ideation and attempts among pathological gamblers trying to quit, and to identify predictors of suicidal ideation and attempts, with a particular emphasis on mood and substance use disorders. A community sample of 101 individuals with gambling problems who had made a recent quit attempt was assessed using structured instruments. Of these, 28.7% reported no history of suicide ideation or attempts, 38.6% reported having only thoughts of suicide, and 32.7% reported a suicide attempt. Ideation predated the onset of gambling problems by an average of greater than ten years. History of ideation was increasingly likely with a greater severity of gambling problem as determined by DSM criteria. Those experiencing ideation were also more likely to over gamble on gambling days and five times more likely to have a history of depression. Substance abuse history was the only factor that distinguished between individuals who had a history of suicide attempts versus ideation only. Having a drug history was related to a more than six times greater likelihood of having made a suicide attempt. Gambling-related suicide attempts were relatively rare-21.2% of attempters, or 7% of the total sample. These findings are consistent with the common factor model of etiology in which the suicidality of gambling is related to prior mental health disorders. More research on the relationship between alcohol and other drug disorders and their complex relationship to pathological gambling and suicide is crucial.
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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 | 0.003 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".