Risk Factors for Cannabis-Related Mental Health Harms in Older Adults: A Review
Bibliographic record
Abstract
Objectives: This paper reviews research on the topic of cannabis use and mental health harms in older adults and illustrates potential contributing factors and special clinical considerations for working with this population. Known risk factors for cannabis-related mental disorders and mental health problems are outlined, first for the general population and then specifically for older adults.Methods: Studies were identified through online databases using a variety of search words. Articles were included in the review if they were peer-reviewed or published by a reputable national organization, published in English, and were pertinent to the topic of mental health harms of cannabis use.Results: Risk factors that emerged from the literature review aligned with the following categories: (1) patterns of use (i.e., potency of product, frequency of use), (2) personal characteristics (i.e., age, sex, social demographics), (3) psychosocial constructs (motivations, perceptions), and (4) morbidities (mental health, medication interactions).Conclusions: Frequent use was associated with increased risk for mental health consequences related to cannabis use. Certain motives for use (i.e., using to cope, using as a sleep aid) may increase susceptibility to cannabis-related harms, although more empirical work is required. Mental health conditions may predispose to cannabis-related harms through a variety of mechanisms, including increased vulnerability for cannabis-related psychiatric disorders, poorer prognosis for preexisting psychiatric disorders, and possibility of cannabis-medication interactions. Personal characteristics (younger age, being male, lower socioeconomic status) predict more frequent cannabis use, which may dispose to adverse outcomes.Clinical Implications: Predictors of cannabis-related harms hold relevance for public health messaging, as well as clinical interventions. Understanding how cannabis interacts with sociodemographic factors, mental health morbidities, and medications is crucial in providing accurate guidance to patients about their recreational cannabis use and in informing prescriber decisions about medicinal cannabis.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".