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Record W7081571856 · doi:10.17605/osf.io/ec35f

Cannabis harm reduction in settings where cannabis is illegal: an international Delphi consensus study

2025· other· en· W7081571856 on OpenAlexaboutno aff

Bibliographic record

VenueOpen Science Framework · 2025
Typeother
Languageen
FieldComputer Science
TopicGeochemistry and Geologic Mapping
Canadian institutionsnot available
Fundersnot available
KeywordsCannabisHarm reductionAbstinenceEffects of cannabisPopulationPublic healthHarm

Abstract

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Cannabis use carries several mental and physical health risks, including greater prevalence and more severe symptomatology of psychosis, depression, and anxiety, respiratory problems, and physical harms from driving while intoxicated [1, 2]. While abstinence is generally the safest option for reducing cannabis-related health risks [3], 4.1% of the global population and 7.6% of the UK population report using cannabis [4] and cessation of cannabis use can be difficult to achieve. These statistics highlight the need for cannabis harm reduction - a pragmatic approach focused on strategies to minimise drug-related harms without necessarily promoting abstinence. Harm reduction is a well-developed concept for most drugs but is perhaps most widely understood and applied for opioid use. Public health initiatives like needle and syringe exchange services, safe injection sites, and take-home naloxone programmes have been implemented in several countries, including the UK, and have been shown to reduce opioid-related mortality and morbidity [5-7]. In the nicotine field, vaping and other heat-not-burn tobacco products are increasingly considered safer alternatives for people who smoke cigarettes [8, 9]. Similarly, some of the most prevalent and serious cannabis-related harms, including cannabis use disorder, psychosis, and respiratory problems, may be prevented through safer use practices [3, 10-14]. However, despite the prevalence of and harms associated with cannabis use, “cannabis harm reduction” is conceptually less well-defined. Examples from the Lower-risk Cannabis Use Guidelines (LRCUG), which are modifiable use behaviour-related risk factors identified by international cannabis expert teams, include choosing products with low levels of Δ9- tetrahydrocannabinol (THC), the main psychoactive compound in cannabis, and choosing consumption methods like vaporisers, e-cigarette devices, and edibles over smoking [3, 15, 16]. Guidelines such as the LRCUG provide tangible and evidence-based information to people who use cannabis (PWUC) about how they can reduce their risks from using cannabis, without necessarily having to stop completely. However, while the LRCUG and their recommendations are not limited to settings where cannabis is legal for use and involve quality-regulated supply, there is also a need for harm reduction advice specifically targeted at people who use cannabis in illegal and unregulated markets, where reliable information about products is typically unavailable and additional sources of harm might exist. For instance, in one study, cannabis vape liquids seized from English schools were found to rarely contain THC and instead contained more dangerous substances such as synthetic cannabinoids [17]. This illustrates that vaping e-liquids may pose a higher risk than smoking cannabis flower in settings where cannabis is illegal. In this instance, relevant actionable harm reduction advice might be to use cannabis flower with a medical-grade dry-herb vaporiser, or to test THC vape liquid at a drug checking service (where available) before using. Similarly, it can be difficult for PWUC to know the THC content in cannabis purchased from an illicit market, or even access low-THC products in an illicit market saturated with high-potency cannabis [9], which means that using low-potency products is distinctly difficult to achieve in such settings. This study will use a Delphi method to develop consensus-based cannabis harm reduction guidelines aimed for people consuming cannabis in settings where supply is illegal. The identification of key harm reduction behaviours is necessary to inform the development of targeted interventions that decrease harmful use practices in such settings, ultimately reducing the health burden of cannabis use. 1. Hoch, E., et al., Cannabis, cannabinoids and health: a review of evidence on risks and medical benefits. Eur Arch Psychiatry Clin Neurosci, 2024. 2. Solmi, M., et al., Balancing risks and benefits of cannabis use: umbrella review of meta-analyses of randomised controlled trials and observational studies. Bmj, 2023. 382: p. e072348. 3. Fischer, B., et al., Lower-Risk Cannabis Use Guidelines (LRCUG) for reducing health harms from non-medical cannabis use: A comprehensive evidence and recommendations update. Int J Drug Policy, 2022. 99: p. 103381. 4. United Nations Office on Drugs and Crime, World Drug Report 2024. 2024. 5. Hurley, S.F., D.J. Jolley, and J.M. Kaldor, Effectiveness of needle-exchange programmes for prevention of HIV infection. Lancet, 1997. 349(9068): p. 1797-800. 6. Levengood, T.W., et al., Supervised Injection Facilities as Harm Reduction: A Systematic Review. Am J Prev Med, 2021. 61(5): p. 738-749. 7. McDonald, R. and J. Strang, Are take-home naloxone programmes effective? Systematic review utilizing application of the Bradford Hill criteria. Addiction, 2016. 111(7): p. 1177-87. 8. Simonavicius, E., et al., Heat-not-burn tobacco products: a systematic literature review. Tob Control, 2019. 28(5): p. 582-594. 9. Erku, D., et al., Nicotine vaping products as a harm reduction tool among smokers: Review of evidence and implications for pharmacy practice. Res Social Adm Pharm, 2020. 16(9): p. 1272-1278. 10. Borodovsky, J.T., et al., Characterizing cannabis use reduction and change in functioning during treatment: Initial steps on the path to new clinical endpoints. Psychol Addict Behav, 2022. 36(5): p. 515-525. 11. Di Forti, M., et al., Daily use, especially of high-potency cannabis, drives the earlier onset of psychosis in cannabis users. Schizophr Bull, 2014. 40(6): p. 1509-17. 12. Petrilli, K., et al., Association of cannabis potency with mental ill health and addiction: a systematic review. Lancet Psychiatry, 2022. 9(9): p. 736-750. 13. Sherman, B.J., et al., Evaluating cannabis use risk reduction as an alternative clinical outcome for cannabis use disorder. Psychol Addict Behav, 2022. 36(5): p. 505-514. 14. Stone, B.M. and B.J. Sherman, Is it time for a cannabis harm reduction approach? Commentary on Sherman et al. (2022) and Borodovsky et al. (2022). Psychol Addict Behav, 2023. 37(5): p. 709-712. 15. Fischer, B., et al., Lower-Risk Cannabis Use Guidelines: A Comprehensive Update of Evidence and Recommendations. Am J Public Health, 2017. 107(8): p. e1-e12. 16. Fischer, B., et al., Lower Risk Cannabis use Guidelines for Canada (LRCUG): a narrative review of evidence and recommendations. Can J Public Health, 2011. 102(5): p. 324-7. 17. Cozier, G., et al., Synthetic cannabinoids in e-cigarettes seized from English schools. Addiction, 2025.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication, Open science, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.073
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0100.003
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.020
GPT teacher head0.327
Teacher spread0.307 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreOther

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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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