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Enregistrement W1536395067 · doi:10.1111/j.1360-0443.2007.01884.x

A CROSS‐CUTTING RESEARCH AGENDA ON ALCOHOL, TOBACCO AND OTHER DRUGS: WHERE TO START?

2007· letter· en· W1536395067 sur OpenAlexaffabout
Tim Stockwell

Notice bibliographique

RevueAddiction · 2007
Typeletter
Langueen
DomaineMedicine
ThématiqueSubstance Abuse Treatment and Outcomes
Établissements canadiensUniversity of Victoria
Organismes subventionnairesnon disponible
Mots-clésMedical prescriptionPsychoactive substanceSubstance useMedicineEnvironmental healthPsychiatryBusinessPsychologyPublic relationsPolitical sciencePharmacology

Résumé

récupéré en direct d'OpenAlex

Like Cook and Reuter [1], my impression of the research that informs current understanding of psychoactive substance use and which shapes policy responses is that it largely comprises studies on a single substance type. The last hundred years has seen an explosion in the availability of psychoactive substances; some are developed prescribed medications for the treatment of mental health problems and others are manufactured illegally, often in home-based laboratories. The expansion of international transport, communication and trade has ensured that these new substances as well as more traditional ones are delivered efficiently to drug markets the world over. Contemporary patterns of substance use, especially among younger people, seem to increasingly involve the use of multiple substances both over time and on the same occasion [2–4], so that legal, illegal and prescription drug markets are increasingly intertwined. Furthermore, there is strong evidence that patterns of multiple substance use are predictive of increased risk of harms [5,6]. Cook and Reuter [1] do the substance use field a service, therefore, with their observation that it is time for a cross-cutting research agenda spanning the alcohol, tobacco and other drug fields. While the prevention field has, for some time, recognized the need to address common risk and protection factors regarding the hazardous use of different psychoactive substances and also other problem behaviours [7–9], Cook and Reuter make the undeniable point that much research funding and hence research practice to date has occurred within substance-specific silos. A truly cross-cutting research agenda spanning all psychoactive substances would be an enormous and daunting undertaking. The questions discussed briefly below are but two in a potentially very long list of useful places to start. How well do we know the extent to which the markets for different legal and illegal drugs compete and/or complement each other for different population groups? One practical example close to home is that in Canada it has been observed recently that teenagers are more likely to use cannabis on a regular basis than they are tobacco [10]. Could this be one consequence of Canada's well-earned reputation for world-class tobacco control measures? There are also a few studies suggesting direct connections between alcohol, cannabis and other drug markets [11,12], but we need further studies to understand the extent to which this and other similar cross-substitution occurs. One benefit will be to appreciate more fully the public health and safety consequences of succeeding with the control and regulation of one particular substance in terms of the total impact across all psychoactive substances. Much has been written about the importance of measuring patterns of alcohol consumption if we are to understand and predict acute and chronic alcohol-related harms more clearly [13]. However, if combined alcohol and other substance use carries a higher risk of adverse consequences and such combined use is becoming increasingly common, it follows that we need to have the capacity to measure patterns of combined use. Unfortunately, most population health surveys ask separate sets of quantity–frequency questions for each major type of substance, usually applying to a 12-month period, and it is impossible to identify simultaneous use patterns [13–15]. One solution developed by Earleywine & Newcombe [4] involves asking about every possible permutation of combined use from a half-dozen commonly used psychoactive substances. Apart from beingtime-consuming, this approach also restricts enquiry to a range of already well-known drugs and will fail to pick up new emerging drugs. Recent pilot work in Vancouver for a questionnaire on young people's use of ‘club drugs’ started with a list of 15 common and well-known substances, but the first 20 subjects provided names of a further 30 they had used recently. One approach we are using to capture this diversity involves focused questions on recent occasions of substance use which can pick up the quantities and types of legal and illegal drugs used in combination, as well as information on context of use. Recent recall approaches for alcohol can have the advantage of delivering more complete recall of consumption and can capture aspects of the drinks markets of special relevance for public health and safety purposes [16]. There are many other possible fundamental research questions in need of more attention, such as why do people use different combinations of drugs in the context of different settings and activities? How can the burden of illness associated with combined substance use be estimated? What factors influence transitions between preferences for low-risk to high-risk substances? Cook & Reuter [1] are to be applauded for ‘coming out’ as having been practising substance-specific researchers for the bulk of their eminent careers and for throwing down the gauntlet for the addictions field to begin developing a truly cross-cutting research agenda.

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)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict), Charge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Commentaire · Signal consensuel: aucune
Score de désaccord entre enseignants0,651
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

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

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 Opus0,098
Tête enseignante GPT0,395
Écart entre enseignants0,297 · 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

Citations7
Publié2007
Routes d'admission2
Résumé présentoui

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