Non-medical use of prescription drugs by young men: impact of potentially traumatic events and of social-environmental stressors
Bibliographic record
Abstract
Background: Non-medical use of prescription drugs (NMUPD) is an increasing phenomenon associated with physical and psychological consequences. This study investigated the effects of distinct forms of stress on NMUPD.Methods: Data from 5308 young adult men from the Swiss cohort study on substance use risk factors (C-SURF) were analysed regarding NMUPD of sleeping pills, tranquilizers, opioid analgesics, psychostimulants, and antidepressants. Various forms of stress (discrete, potentially traumatic events, recent and long-lasting social-environmental stressors) during the period preceding the NMUPD assessment were measured. Backward log-binomial regression was performed and risk ratios (RR) were calculated.Results: NMUPD was significantly associated with the cumulative number of potentially traumatic events (e.g. for opioid analgesics, RR = 1.92, p < .001), with problems within the family (e.g. for sleeping pills, RR = 2.45, p < .001), and the peer group (e.g. for tranquilizer use, RR = 2.34, p < .01). Factors describing family functioning in childhood showed very few significant associations. Sexual assault by acquaintances was associated only with use of sleeping pills (RR = 2.91, p p <.01); physical assault by acquaintances was not associated with NMUPD. Physical (e.g. for psychostimulants, RR = 2.01, p < .001) or sexual assaults (e.g. for antidepressants, RR = 4.64, p < .001) perpetrated outside the family context did show associations with several drug categories.Conclusion: NMUPD appears to be more consistently associated with discrete and potentially traumatic events and with recent social-environmental stressors than with long-lasting stressors due to family functioning during childhood and youth. Physical and sexual assaults perpetrated by strangers showed more associations with NMUPD than those perpetrated by a family member.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".