The gendered relationship between illicit substance use and self-harm in university students
Bibliographic record
Abstract
PURPOSE: To estimate associations between multiple forms of substance use with self-harming thoughts and behaviours, and to test whether gender is an effect modifier of these associations, both independently and along with perceived risk of cannabis use. METHODS: Data were drawn from the 2018 Norwegian Students' Health and Wellbeing Study (SHoT 2018). A national sample of n = 50,054 full-time Norwegian students (18-35 years) pursuing higher education completed a cross-sectional student health survey, including questions on past-year self-harm: non-suicidal thoughts of self-harm, non-suicidal self-harm, suicidal thoughts, and suicide attempt. Students reported their frequency of past-year alcohol use (range: never to ≥ 4 times/ week), illicit substance consumption, and perceived risk of cannabis use. The AUDIT and CAST screening tools measured problematic alcohol and cannabis consumption, respectively. We used logistic regression modelling adjusted for age, symptoms of depression and anxiety, and financial hardship (analytic sample range: n = 48,263 to n = 48,866). RESULTS: The most frequent alcohol consumption category (≥ 4 times/ week) was nearly always associated with more than a two-fold increased likelihood of self-harm. Less frequent alcohol consumption was associated with reduced odds of suicidal thoughts [monthly or less: OR = 0.87 (95% CI: 0.75-1.00), 2-4 times/month: OR = 0.79 (95% CI: 0.69-0.91), and 2-3 times/ week: OR = 0.83 (95% CI: 0.71-0.98)]. Problematic alcohol consumption was associated with most outcomes: odds ranging from 1.09 (95% CI: 1.01-1.18) for suicidal thoughts to 1.33 (95% CI: 1.00-1.77) for suicide attempt. There was evidence of multiple illicit substance by gender interactions: consumption of all but one illicit substance category (other drug use) was associated with all four forms of self-harm for women, but findings among men were less clear. Among men, only one illicit substance category (stimulant) was associated with most forms of self-harm. Women, but not men, who perceived cannabis use as a health risk were more likely to experience non-suicidal thoughts as cannabis consumption increased, and with harmful consumption patterns. CONCLUSION: Frequent alcohol consumption is associated with increased risk of self-harm and suicidality for young women and men. Associations between illicit substance use and self-harm and suicidality appear stronger in women compared to men.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".