Changes in Local Community Spatial Trends of Motor Vehicle Accidents Near Cannabis Dispensaries after Recreational Cannabis Legalization
Bibliographic record
Abstract
Introduction: In recent years, the impact of recreational cannabis legalization (RCL) on road safety and motor vehicle accidents (MVAs) has become a growing area of research, given increasing cannabis legalization and the impact of cannabis on motor control and attention. In 2023, Connecticut legalized recreational cannabis, and this study explored changes in MVAs both in a statewide analysis and in the local vicinity of recreational cannabis dispensaries. Materials and Methods: We conducted an ecological study to assess the impact of recreational cannabis dispensaries on MVAs in Connecticut after legalization on January 10, 2023. Using crash data from Connecticut and Maryland (as a control) for two 24-week periods before and after legalization, we performed a difference-in-differences analysis with negative binomial regression, controlling confounders. At the dispensary level, we compared MVAs within an 800-m radius 8 weeks before and after opening, employing interrupted time series analysis with negative binomial mixed-effects regression models. Results: In the statewide analysis comparing Connecticut with Maryland over two 24-week periods before and after RCL in Connecticut, no significant effect on MVAs was found after adjusting for autocorrelation and seasonal variations (interaction term coefficient = −0.0391, p = 0.0696). In the local analysis, examining accident rates within an 800-m radius of 13 dispensaries over 8 weeks before and after their openings, the negative binomial mixed-effects model showed no significant change (incidence rate ratio = 1.10, 95% confidence interval: 0.74–1.64, p = 0.63). Discussion: These findings suggest that cannabis legalization and dispensary openings did not significantly impact motor vehicle accident rates during the study period.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".