Impacts of medical and non-medical cannabis on the health of older adults: Findings from a scoping review of the literature
Bibliographic record
Abstract
BACKGROUND: Cannabis legalization has enabled increased consumption in older adults. Age-related mental, physical, and physiological changes may lead to differences in effects of cannabis in older adults compared to younger individuals. OBJECTIVE: To perform a scoping review to map the evidence regarding the health effects of cannabis use for medical and non-medical purposes in older adults. METHODS: Electronic databases (MEDLINE, Embase, PsycINFO, Cochrane Library) were searched for systematic reviews (SRs), randomized controlled trials (RCTs) and non-randomized/observational studies (NRSs) assessing the health effects and associations of cannabis use (medical or non-medical) in adults ≥ 50 years of age. Included studies met age-related inclusion criteria or involved a priori identified health conditions common among older adults. Records were screened using a liberal accelerated approach and data charting was performed independently by two reviewers. Descriptive summaries, structured tables, effect direction plots and bubble plots were used to synthesize study findings. FINDINGS: From 31,393 citations, 133 publications describing 134 unique studies (26 SRs, 36 RCTs, 72 NRSs) were included. Medical cannabis had inconsistent therapeutic effects in specific patient conditions (e.g., end-stage cancer, dementia), with a number of studies suggesting possible benefits while others found no benefit. For medical cannabis, harmful associations outnumbered beneficial, and RCTs reported more negative effects than NRSs. Cannabis use was associated with greater frequencies of depression, anxiety, cognitive impairment, substance use and problematic substance use, accidents/injuries, and acute healthcare use. Studies often were small, did not consistently assess harms, and did not adjust for confounding. DISCUSSION: The effects of medical cannabis are inconsistent within specific patient conditions. For older adults, generally, the available evidence suggests cannabis use may be associated with greater frequencies of mental health issues, substance use, and acute healthcare use, and the benefit-to-risk ratio is unclear. Studies with a balanced assessment of benefits and harms may guide appropriate public health messaging to balance the marketing pressures of cannabis to older adults.
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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 | 0.019 | 0.085 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.027 | 0.023 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 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".