On the relationship between epidemiology and policy
Bibliographic record
Abstract
We thank Professor Wayne Hall for his insightful comments 1 on our recent study characterizing cannabis-attributable harms in Canada 2. Although agreeing with his general notion that epidemiological results should not, and cannot, determine general policy questions such as legalization, there are three important linkages which should be taken into consideration. First, the level of policy regulation should be proportionate to the degree of potential harm 3. In other words, substances with greater harm potential should be regulated more than substances with lesser harm potential. Obviously, less regulation of psychoactive substances may lead to their greater availability, and therefore greater harm (as evidenced for alcohol 4 and prescription opioids 5; for Canada see 6), but there are differences in the pharmacological and toxicological properties of the substances 7, 8. An important epidemiological indicator in this regard would be harm per user or harm per heavy user 9, which would also suggest cannabis as having lesser harm potential than alcohol, tobacco or prescription opioids in Canada. Based on the data presented in our study 2, one death per 10 000 users would be expected for cannabis, whereas the corresponding estimates for alcohol, tobacco and prescription opioids would be considerably higher (four, 100 and three deaths per 10 000 users, respectively). Secondly, the type of harm and the kind of risk relations can point to specific policies. Given that the major fatal risks of cannabis use are injuries, and in particular road traffic injuries 10, specific policies (e.g. per se laws) are indicated, independent of the overall legal status of cannabis (i.e. per se laws can be applied to substances that are under prohibition, decriminalized or legalized 11). Another implication is that most of the harm from cannabis relates to heavy users, which implies the need for specifically targeted policies 12 compared to substances where the prevention paradox applies 13. Finally, comparisons of epidemiological outcomes through comparative risk assessments should always take the knowledge base into consideration. For instance, there is far more accumulated evidence on alcohol and chronic disease risks 14, 15 than similar evidence on cannabis 16. This is due, in part, to the greater availability of alcohol, and thus data could be integrated more easily into large medical cohort studies. This situation will improve with a more evidence-based approach to drug policies in the future. Therefore, policies can, by no means, be derived exclusively from epidemiological results per se, but should be informed by such results. 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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".