The Experiences of Older Adults with Cannabis and Mental Health: A Scoping Review of the Literature
Bibliographic record
Abstract
Abstract Following the 2018 federal legalization of cannabis in Canada, there was a drastic increase in older adults reporting marijuana use. Most cannabis research today focuses on children and young adults, however, it is important to acknowledge the potential harms in seniors as well. Aging and substance use presents unique considerations, such as the interactions between cannabis and chronic conditions, multiple comorbidities, polypharmacy, and mental health. The goal of this scoping review was to analyze the literature that addresses mental health outcomes of seniors who use cannabis, in order to answer the main research question: What is the relationship between older adults’ use of cannabis and mental health? Following Arksey and O’Malley’s five-stage framework, 10 electronic databases were searched along with a hand search of references. The search revealed 7000+ peer-reviewed and grey literature sources. 233 full-text sources were assessed for eligibility, with a total of 25 articles included. Thematic content analysis produced four major themes which addressed: (1) Usage characteristics; (2) User characteristics; (3) Outcomes; and (4) Physical and mental health considerations. Findings from this scoping review are positioned in terms of their implications for research, practice, and policy. While more in-depth, qualitative methods are required to develop further research, several harm-reduction strategies may be immediately utilized by both users and healthcare practitioners. It is critical that older adults and their physicians are able to make cannabis-related decisions with evidence-informed guidance to prevent problematic cannabis use and ensure positive mental health outcomes.
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 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 | 0.011 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.017 | 0.015 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 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".