Patterns of simultaneous polysubstance use in drug using university students
Bibliographic record
Abstract
Simultaneous polysubstance use (SPU) is a common phenomenon, yet little is known about how various substances are used with one another. In the present study 149 drug-using university students completed structured interviews about their use of various substances. For each substance ever used, participants provided details about the type, order and amount of all substances co-administered during its most recent administration. Alcohol, tobacco and cannabis were frequently co-administered with each other and with all other substances. Chi-squared tests revealed that when alcohol was used in combination with any of cannabis, psilocybin, MDMA, cocaine, amphetamine, methylphenidate (ps < 0.01) or LSD (p < 0.05) its initial use preceded the administration of the other substance. Paired samples t-tests revealed that when alcohol was used with cocaine (p < 0.01) or methylphenidate (p < 0.05) it was ingested in greater quantities than when used in their absence. Patterns of cannabis use were not systematically related to other substances administered. Finally, using one-sample t-tests, tobacco use was demonstrated to be increased relative to 'sober' smoking rates when used with alcohol, cannabis, psilocybin, MDMA, cocaine, amphetamine (ps < 0.001), LSD (p < 0.01) or methylphenidate (p < 0.05). Results suggest that many substances are routinely used in a SPU context and that the pattern in which a substance is used may be related to other substances co-administered.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".