Substituting cannabis for prescription drugs, alcohol and other substances among medical cannabis patients: The impact of contextual factors
Bibliographic record
Abstract
INTRODUCTION AND AIMS: Recent years have witnessed increased attention to how cannabis use impacts the use of other psychoactive substances. The present study examines the use of cannabis as a substitute for alcohol, illicit substances and prescription drugs among 473 adults who use cannabis for therapeutic purposes. DESIGN AND METHODS: The Cannabis Access for Medical Purposes Survey is a 414-question cross-sectional survey that was available to Canadian medical cannabis patients online and by hard copy in 2011 and 2012 to gather information on patient demographics, medical conditions and symptoms, patterns of medical cannabis use, cannabis substitution and barriers to access to medical cannabis. RESULTS: Substituting cannabis for one or more of alcohol, illicit drugs or prescription drugs was reported by 87% (n = 410) of respondents, with 80.3% reporting substitution for prescription drugs, 51.7% for alcohol, and 32.6% for illicit substances. Respondents who reported substituting cannabis for prescription drugs were more likely to report difficulty affording sufficient quantities of cannabis, and patients under 40 years of age were more likely to substitute cannabis for all three classes of substance than older patients. DISCUSSION AND CONCLUSIONS: The finding that cannabis was substituted for all three classes of substances suggests that the medical use of cannabis may play a harm reduction role in the context of use of these substances, and may have implications for abstinence-based substance use treatment approaches. Further research should seek to differentiate between biomedical substitution for prescription pharmaceuticals and psychoactive drug substitution, and to elucidate the mechanisms behind both. [Lucas P, Walsh Z, Crosby K, Callaway R, Belle-Isle L, Kay B, Capler R, Holtzman S. Substituting cannabis for prescription drugs, alcohol, and other substances among medical cannabis patients: The impact of contextual factors. Drug Alcohol Rev 2016;35:326-333].
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".