RESPONSE TO COMMENTARIES: MOVING TOWARDS AN EVIDENCE‐BASED POLICY AROUND CANNABIS USE
Notice bibliographique
Résumé
We are glad that our invited commentary [1] on the relationship between cannabis evidence and cannabis policy has stimulated debate, and we are grateful to our scientific colleagues for their thoughtful responses [2–6]. These responses raise more issues than we can address adequately here, so we will stick to the main points. Our paper had two aims. The first was to examine, and attempt to understand, the recent scientific debate around possible cannabis harms. The second was to discuss what a policy around cannabis based on this evidence might look like. We recognize that political support for evidence-based policy in this context may be, at best, rhetorical. Some of our commentators criticize our focus on evidence around cannabis and psychosis. This seems a little unfair because, for the past decade, and not just in the United Kingdom, this possible harm of cannabis use has driven the policy debate [7]. David Fergusson [2] was unhappy with our distinction between evidence on psychotic symptoms and evidence on schizophrenia. Again, this criticism seems misguided—we did not ignore the former, but the fact that we accorded a different status to the latter is simply normal epidemiological practice. In the same way, a cardiovascular epidemiologist would accord a different status to associations between stress and chest pain compared to objective evidence of coronary vascular disease [8,9]. Professor Fergusson [2] has also misunderstood our critique of the cannabis psychosis/schizophrenia evidence if he thinks it rests on ‘increasingly elaborate’ arguments. It rests now, and always has, on a very simple argument. Apparent independent effects of cannabis use on risk of psychosis may be due to residual confounding and measurement error [10]. That is not to say they are not causal—they might be, but it is simply impossible to know. Most scientists, including our commentators, agree on this. All we appear to be arguing about is the level of uncertainty. What about other possible harms? Cannabis use has been associated with several adverse outcomes, as listed by Professor Wittchen [3], although as we have discussed elsewhere, in relation to most of these the strength of the evidence that the association has a causal basis is weaker than in the case of psychosis [11]. We agree with Hall & Degenhardt [4] that cannabis dependence can be added safely to the list of cannabis harms and apologize if we appeared to downplay the importance of this. Naively, we thought that another point we could all agree upon would be the harm that cannabis causes, through concomitant tobacco use. Can we be very clear that our assertions around tobacco and cannabis bear absolutely no relation to any pet thesis we are trying to promote and are not influenced by any ‘wish bias’? Perhaps we are hoist by our own petard here: guilty of over-interpreting the limited empirical evidence that a substantial proportion of cannabis users smoke cannabis mixed with tobacco, and that for many of them their cannabis use reinforces their tobacco use [12–14]. Obviously, if in most of the cannabis-smoking world, cannabis is not smoked with tobacco then our assertions in this regard are unlikely to be true. However, even if we cannot agree on the precise hierarchical structure of a list of possible cannabis harms we seem to have come to a point where we can agree on the list and the fact that, based on the precautionary principle, we have a basis to advocate prevention. The question then is how do we pursue this goal? We believe that any policy be judged on the simple criteria used commonly to guide decisions around public health interventions: are they cost-effective, do they cause more good than harm and are they acceptable to people at whom they are aimed? Robert MacCoun [5] found our suggestion that cannabis prohibition probably failed this test ‘awfully brash’. This surprised us; all our respondents who commented on the issue agreed that there is no strong evidence that prohibition reduces cannabis use. Alongside this lack of evidence of benefit, prohibition also incurs considerable costs [15,16]. As MacCoun [15] suggests pessimistically, the alternatives could always be worse. This is true, and is the reason why the alternatives should be evaluated rigorously. None.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,002 | 0,005 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; les deux têtes enseignantes s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».