Effects of medical and non-medical cannabis use in older adults: protocol for a scoping review
Bibliographic record
Abstract
INTRODUCTION: With its legalisation and regulation in Canada in 2018, the proportion of Canadians reporting cannabis use in 2019 increased substantially over the previous year, with half of new users being aged 45+ years. While use in older adults has been low historically, as those born in the 1950s and 1960s continue to age, this demographic will progressively have more liberal attitudes, prior cannabis exposure and higher use rates. However, older adults experience slower metabolism, increased likelihood of polypharmacy, cognitive decline and chronic physical/mental health problems. There is a need to enhance knowledge of the effects of cannabis use in older adults. The following question will be addressed using a scoping review approach: what evidence exists regarding beneficial and harmful effects of medical and non-medical cannabis use in adults >50 years of age? Given that beneficial and harmful effects of cannabis may be mediated by patient-level (eg, age, sex and race) and cannabis-related factors (eg, natural vs synthetic, consumption method), subgroup effects related to these and additional factors will be explored. METHODS AND ANALYSIS: Methods for scoping reviews outlined by Arksey & O'Malley and the Joanna Briggs Institute will be used. A librarian designed a systematic search of the literature from database inception to June 2019. Using the OVID platform, Ovid MEDLINE will be searched, including Epub Ahead of Print and In-Process and Other Non-Indexed Citations, Embase Classic+Embase, and PsycINFO for reviews, randomised trials, non-randomised trials and observational studies of cannabis use. The Cochrane Library on Wiley will also be searched. Eligibility criteria will be older adult participants, currently using cannabis (medical or non-medical), with studies required to report a cannabis-related health outcome to be eligible. Two reviewers will screen citations and full texts, with support from artificial intelligence. Two reviewers will chart data. Tables/graphics will be used to map evidence and identify evidence gaps. ETHICS AND DISSEMINATION: This research will enhance awareness of existing evidence addressing the health effects of medical and non-medical cannabis use in older adults. Findings will be disseminated through a peer-reviewed publication, conference presentations and a stakeholder meeting. TRIAL REGISTRATION NUMBER: DOI 10.17605/OSF.IO/5JTAQ.
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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 | 0.066 | 0.060 |
| Meta-epidemiology (narrow) | 0.006 | 0.005 |
| Meta-epidemiology (broad) | 0.016 | 0.017 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.075 | 0.012 |
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".