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Enregistrement W2052164592 · doi:10.1111/j.1360-0443.2011.03487.x

NUTT ET AL.'S HARM SCALES FOR DRUGS-ROOM FOR IMPROVEMENT BUT BETTER POLICY BASED ON SCIENCE WITH LIMITATIONS THAN NO SCIENCE AT ALL

2011· letter· en· W2052164592 sur OpenAlexaffabout
Benedikt Fischer, Perry Kendall

Notice bibliographique

RevueAddiction · 2011
Typeletter
Langueen
DomaineMedicine
ThématiqueHIV, Drug Use, Sexual Risk
Établissements canadiensCentre for Addiction and Mental HealthMinistry of HealthSimon Fraser University
Organismes subventionnairesnon disponible
Mots-clésOperationalizationHarmHarm reductionContext (archaeology)PsychologySocial psychologyEpistemologyPositive economicsMedicinePublic healthEconomicsPhilosophy

Résumé

récupéré en direct d'OpenAlex

In their commentary, Caulkins et al. 1 reflect on Nutt and colleagues' recent efforts to develop evidence-based scales to assess the harms of drugs, or to rank drugs on the basis of their harmfulness 2, 3. Caulkins et al. vigorously criticize these efforts as ‘fundamentally flawed both conceptually and methodologically’ as well as ‘misguided in principle and particulars’. We share some aspects of the methodological and conceptual criticisms offered by Caulkins et al. vis-à-vis Nutt et al.'s proposed harm scales. We agree, for example, that it is difficult, and can be empirically counterproductive, to try to reduce the harms of drugs into a single indicator measure. The harms and risks related to drugs are clearly multi-dimensional and occur on a variety of levels, and as such would be better expressed by a harm matrix, as has been suggested by others 4, 5. We ourselves, in an attempt to operationalize definitions of ‘harm’ as a conceptual foundation for empirically measuring ‘harm reduction’, proposed such a multi-dimensional matrix more than a decade ago 6. This proposal emphasized the need for measurements of harm at both individual and social levels, also on different axes differentiating acute and long-term harms. We also emphasize—or reiterate—that it is difficult, if not impossible, to assess, quantify or compare any drug's harm independently of its social context. In other words, the currently perceived, assessed or actual harms of alcohol, tobacco or heroin, as examples, are both influenced and constrained by the (desired and undesired) effects that current regulatory or market systems of control have on them 7. That is, as Caulkins notes, harms (or benefits) are highly dependent upon social milieu. However, having acknowledged these potential limitations in Nutt et al.'s models and the methodological weaknesses, and while agreeing on the need for further scientific debate to refine and advance the science of assessing harms as part of the evidentiary base for policy formulation, we submit that these reservations and concerns are of limited relevance when compared to the current lack of any scientific base for present drug control policy. Our opinion is that these methodological concerns definitely ought not to constitute the grounds for categorical dismissal or rejection as advocated by Caulkins et al. Using our own country, Canada, as an example, it is readily apparent that drug scheduling within the Controlled Drugs and Substances Act (CDSA) 8, 9 or provincial and territorial regulatory control systems have little to no footing in scientific evidence; neither do they follow elementary principles informed by empirical logic. If we assume that public health and welfare should be guiding principles for good and desirable psychoactive substance control policy we would, for example, not expect to see the third most commonly used drug (cannabis) to be scheduled and regulated alongside drugs such as heroin and cocaine, while alcohol and tobacco are not only legally available but are openly traded in Canadian society and cause thousands of cases of deaths and injuries each year 7. In comparison, cannabis consumption has zero directly attributable mortality and relatively little major associated morbidity in the majority of users 10, 11. Historically, drug scheduling in Canada originated as a tool of socio-economic control of non-white minority groups, and hence the original drugs included successively in the drug control schedule were opium, cocaine and cannabis (1908–1925) 12, 13. The conceptual framework laid then, and which persists today, had neither public health, nor pharmacology, nor any attempt of rigorous harm quantification as a foundation. Interestingly, tobacco prohibition was considered briefly in Canada during this period, but not considered feasible given its popularity among the dominant white and Anglo-Saxon middle class 14, 15. Alcohol prohibition was approved in Canada by referendum in the late 19th century, yet was never enacted by the federal government (due mainly to resistance from Quebec), and was later imposed on supply largely as a consequence of World War I 16. Thus from a view of current public health and policy perspectives, applying Nutt's harm scales, flawed and limited as they may be, would constitute a quantum leap of progress towards evidence-based and more rational drug policy in Canada and elsewhere. In applying Nutt's harm scales, drug scheduling and control would, to a substantial extent, be informed by scientific evidence of risk and harms from drugs, rather than rooted in the anachronistic skeletons of socio-economic class and control, which arguably may do as much (or more) harm to public health in Canada as the drugs it is supposed to control 17-19. The benefits from grounding Canadian drug control policy in Nutt et al.'s harm scales could be expected to be tangible until at least after their critics have revised and improved them. 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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,001
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Commentaire · Signal consensuel: Commentaire
Score de désaccord entre enseignants0,036
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0010,001
Communication savante0,0000,000
Science ouverte0,0010,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,062
Tête enseignante GPT0,341
Écart entre enseignants0,280 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeSans objet
Domainenon disponible
GenreCommentaire

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations28
Publié2011
Routes d'admission2
Résumé présentoui

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