Patterns and motivations of polysubstance use: a rapid review of the qualitative evidence
Bibliographic record
Abstract
INTRODUCTION: Polysubstance use-the use of substances at the same time or close in time-is a common practice among people who use drugs. The recent rise in mortality and overdose associated with polysubstance use makes understanding current motivations underlying this pattern critical. The objective of this review was to synthesize current knowledge of the reasons for combining substances in a single defined episode of drug use. METHODS: We conducted a rapid review of the literature to identify empirical studies describing patterns and/or motivations for polysubstance use. Included studies were published between 2010 and 2021 and identified using MEDLINE, Embase, PsycINFO and Google Scholar. RESULTS: We included 13 qualitative or mixed-method studies in our analysis. Substances were combined sequentially to alleviate withdrawal symptoms or prolong a state of euphoria ("high"). Simultaneous use was motivated by an intention to counteract or balance the effect(s) of a substance with those of another, enhance a high or reduce overall use, and to mimic the effect of another unavailable or more expensive substance. Self-medication for a pre-existing condition was also the intention behind sequential or simultaneous use. CONCLUSION: Polysubstance use is often motivated by a desire to improve the experience based on expected effects of combinations. A better understanding of the reasons underlying substance combination are needed to mitigate the impact of the current overdose crisis.
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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 | 0.030 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.017 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".