Notice bibliographique
Résumé
Cunningham and colleagues [1] provide what is probably the most compelling evidence to date that precursor regulations, or indeed any supply control strategy, can have significant impacts on the retail market for illicit drugs. This research is particularly interesting if considered in the context of Cunningham's earlier work, which has shown that precursor regulations are followed by significant reductions in methamphetamine-related arrests and hospital admissions [2–4]. Linking these reductions to changes in the retail market for illicit methamphetamine (i.e. decreased purity), and showing that precursor regulations impact specifically on methamphetamine, fortifies the argument that precursor regulations are responsible for reductions in methamphetamine-related harms. This research is ground-breaking, in that it paves the way towards a more sophisticated analysis of how supply reduction interventions impact on harms from illicit drug use. One of the concerns about precursor regulations is the possibility that they may displace, rather than diminish, the supply and demand for drugs (i.e. decreases in the supply for methamphetamine may lead to increases in the supply and use of other drugs) [5]. The approach taken by Cunningham and his colleagues has the potential to assess or dispel such possible unintended consequences of precursor regulations. They demonstrate that no such displacement was apparent with respect to either cocaine or heroin purity. This is an important development, because it demonstrates that strategies which restrict the retail market for one illicit drug do not necessarily have unwanted negative effects on the retail market for other drugs. A caveat in this context, however, is the need to consider the impact of methamphetamine precursor controls on the supply of other synthetic drugs, which are likely to share a more similar supply chain to methamphetamine than crop-based illicit drugs such as heroin and cocaine. While the findings of Cunningham and colleagues are compelling, there is still much scope for improving our understanding of how precursor regulations impact on the retail market for illicit drugs. It is clear from Cunningham's work that this is not always a straightforward relationship. Precursor regulations in Canada appeared to have the unintended negative effect of increasing the purity of methamphetamine in the United States. This was not because the regulation was ineffective in Canada, but because of a competing supply channel for high-purity methamphetamine from Mexico to the United States. Caution is needed when inferring why specific precursor regulations are effective or ineffective, because their impact depends upon the context in which they are delivered. For example, Cunningham speculates that precursor regulations that target small-scale methamphetamine producers are ineffective. This inference assumes that small-scale manufacturers rely on over-the-counter cold and flu remedies while large-scale manufacturers do not—an assumption that may not hold true in all countries. The temporal context of interventions also deserves attention. If two different regulatory measures are implemented in rapid succession, the second may not have an immediate impact on drug purity beyond that produced by the first intervention (i.e. a floor effect). However, conventional wisdom about the diversion of precursor chemicals into clandestine drug manufacture suggests that regulating one source of a precursor leads clandestine chemists to seek alternative sources. In this situation, supplementary regulations are a safeguard against such shifts in the sourcing of precursor chemicals. They may not have a direct impact on methamphetamine purity, but they might prolong the impact of an earlier regulatory intervention. Finally, there has been much debate within the illicit drugs field about the comparative benefits of supply reduction and demand reduction approaches. Undoubtedly both are needed, and this fact has been recognized in international drug control policies [6]. However, there are people who argue that supply control is ineffective in controlling illicit drug use and that resources would be better spent on health and social interventions [7]. The difficulty with countering or confirming such arguments is that the impact of supply reduction strategies is rarely evaluated. Cunningham's work is commendable because it provides a framework for delivering empirical evidence that can be used to shape effective harm minimization strategies, regardless of whether they are delivered by the health sector or via drug law enforcement. 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 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».