We need convincing data to support a public health approach to cannabis regulation
Bibliographic record
Abstract
Agreed measures of cannabis use and impacts are essential in evaluating regulatory change and supporting policy decisions driven by public health evidence. A shared understanding of measures also responds to the call for greater collaboration and coordination in cannabis research. The iCannToolkit provides a collaborative foundation that can be expanded to additional domains and to address emerging and context-specific data needs. The article by Lorenzetti et al. highlights the challenges posed by the lack of agreed minimum standards for quantifying cannabis use or dosage [1]. It also demonstrates a collaborative approach to reach agreement on measures, with the potential for expansion to support a more comprehensive understanding of the impacts of cannabis regulation. Canada's regulated retail cannabis market launched in October 2018, joining only Uruguay and a handful of US states that had previously legalized non-medical sales and use. Canada's Cannabis Act sets out clear objectives for legalization, focused upon public health and public safety [2]. It also mandates that a review of the administration, operation and impact of the act be conducted 3 years following implementation. Currently, in 2021, more than 20 states in the United States have passed or are in the process of passing legislation permitting adult non-medical cannabis use and steps towards legal retail are being taken internationally, including in Mexico, Malta, Switzerland, Luxembourg and the Netherlands. Those considering legalization are looking to learn from the experience of those who have gone before. Also, Canada's legislative review of the Cannabis Act is poised to begin, opening a window during which public health, public safety, academia, government and industry, among others, will attempt to influence regulatory adjustments. To monitor the impact of legalization Canada has invested in two national population surveys, the National Cannabis Survey and the Canadian Cannabis Survey, and has provided funds that are supporting research initiatives across the country [3]. Although many of these studies include measures of consumption, associated risk behaviour and source, there is limited comparability between them. Even Canada's two national surveys use different time-frames and sampling methods to report current rates of use and cannot be compared [4, 5]. In the absence of agreed measures, answers to basic questions concerning changes in consumption, product sources and risk behaviour are subject to variation. Data on prevalence vary according to how a question is asked [6]. That variation allows strategic selection of data that support particular interests, and therefore poses a challenge to objective public health and safety policy decisions. For example, a survey question that asks respondents how often they smoke cannabis may miss the increasing rates of consumption via alternative methods such as vaping and ingestion, resulting in an underestimate and even a perceived decline in overall use. Consistent data regarding the impacts of cannabis use and policy are therefore essential to support policy decisions that reflect public health and safety interests; particularly in the context of increasing industry influence [7]. Policymakers in Canada determining whether the Cannabis Act is tracking towards its objectives, and whether regulatory change is required or appropriate, are faced with the challenge of determining which data to trust. On an international scale, policymakers faced with the question of legalization are looking to determine whether it has increased or mitigated harms, and how. At all levels, the public health sector needs valid and reliable data to make a convincing case to introduce and retain public health restrictions that frustrate the profit interests of the private sector; for example, those on advertising, marketing and taxation [7, 8]. As Lorenzetti et al. acknowledge, the iCannToolkit will not fill all of these knowledge gaps. Standardized, or at least comparable, measurements of cannabis consumption must be accompanied by retail, health and criminal justice data to create a comprehensive picture of the impacts of legalization. Priorities include measures of equity, product sales and illegal market share [6]. The iCannToolkit demonstrates the potential to apply a similar Delphi procedure to generate additional core measures, informed by a comprehensive list of options currently in use across different contexts. Such a list, or menu, is currently in development and is intended to provide a publicly available reference for those interested in measuring cannabis use towards the objective of increasing data sharing and comparability. The variation in regulatory and retail approaches within Canada, across states in the United States and in the planned Swiss pilot studies frames an incredible natural experiment in which to test common measures across settings. A shared understanding of measures also responds to the call for greater collaboration and coordination in cannabis research [6, 9]. The greatest challenge for the field may be in identifying appropriate leadership, capacity and funding to support a coordinated approach. None.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".