Cannabis-Impaired driving: ethical considerations for the primary care practitioner
Bibliographic record
Abstract
Background: Widespread cannabis consumption and recreational cannabis legalization is thought to have led to an increase in motor vehicle accidents, although there currently lacks ethical guidance for primary care practitioners on cannabis-impaired driving.Objective: The aim was to develop an ethical framework for primary care providers on cannabis-impaired driving.Methods: An ethical analysis in the form of a critical interpretive review was undertaken, using a systematic approach to determine the appropriate action to a given situation with evidence to substantiate its claims. The search strategy was designed to answer the research question: What are some ethical concerns for primary care providers to consider when cannabis-impaired driving is suspected? Four databases were searched in December 2021 using keywords related to cannabis, impaired driving, ethics, and primary care. The resulting evidence was synthesized as recommendations for primary care practice.Results: The ethical approach for primary care practitioners in addressing cannabis-impaired driving can be summarized as the duty to always inform, provide care through prevention and harm reduction strategies, and report when necessary. The prevention of cannabis-impaired driving should not fall on the sole responsibility of primary care practitioners. As this review offers a high-level discussion of the ethical considerations in cannabis-impaired driving, specific recommendations will depend upon the legal and policy designations of individual jurisdictions.Conclusion: Ultimately, the practitioner should manage cannabis-impaired driving in a way that fosters the therapeutic relationship in patient-centered care, through motivational discussions, collaboration with specialists, skills for self-management, patient empowerment, and support. KEY MESSAGES Take-Home Points for Primary Care Practitioners in Cannabis-Impaired Driving • For patients who report driving frequently and using cannabis, the frequency of use, dosage, form of cannabis, tolerance levels, and withdrawal symptoms should be discussed, while informing the patient of the risks, harms, and legal consequences associated with cannabis-impaired driving. • The practitioner’s primary responsibility in the cannabis-impaired driving context is to provide care to patients who drive and consume cannabis, which may include referring patients to mental health care to manage addictive or problematic behaviors associated with cannabis use. • Practitioners may have a duty to report cannabis-impaired driving to legal authorities (such as law enforcement) when the user engages in harmful behavior to themselves or others.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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 teacher head, 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".