Review of cannabis reimbursement by workers’ compensation insurance in the U.S. and Canada
Bibliographic record
Abstract
Changing public attitudes about cannabis consumption have currently led 36 U.S. states and the District of Columbia to approve laws that make cannabis available to consumers with qualifying medical conditions. This article reviews the 36 states and the District of Columbia with medical cannabis access laws to determine if the state or the District also allows reimbursement of the costs of cannabis for a work-related health condition under that state's or District's workers' compensation insurance (WCI) laws and administrative regulations. The legal basis for a state allowing or not allowing WCI reimbursement is described. The review found that only six of the 36 states expressly allow cannabis WCI reimbursement, six expressly prohibit it, 14 states do not require reimbursement, and 10 states, and the District of Columbia, are silent on the issue. The article describes the role of the insurer, treating physician, and worker in obtaining WCI reimbursement in the six states that expressly allow cannabis WCI reimbursement. Comparisons are made to how selected Canadian provinces and territories administer cannabis reimbursement under Canada's new national cannabis legalization law. The article discusses the future role of cannabis legalization in the United States and the evolving role of cannabis from an international perspective.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".