A Perspective on the Effectiveness of Interventions for Alcohol and other Substance Use Disorders
Notice bibliographique
Résumé
Together, the 2 papers in this In Review series'- on the effectiveness of interventions for alcohol use disorders (AUDs) represent a comprehensive synthesis of the relevant literature and will serve as the go-to resource for some years for Canadian and international researchers, as well as for government and community decision-makers involved in funding, developing policy, and administering and delivering treatment and support services. As comprehensive as the 2 papers are, Dr Bernard Le Foil and colleagues (see Lev-Ran S et al') and Dr Garth W Martin and Dr Jurgen Rehnr acknowledge several limitations that the reader should keep in mind, most notably the limited attention given to special populations, such as youth, women, older adults, and diverse communities. For some of these subpopulations (for example. First Nations or subpopulations based on gender diversity) the research findings are not substantive enough to draw firm conclusions regarding the most effective interventions. In other instances, such as the treatment of adolescents and youth, it was a matter of keeping the reviews in reasonable scope and managing space limitations. Certainly the literature on the treatment of adolescents and youth is very well developed-' and strong enough to warrant the same general conclusion as presented in the 2 review papers'2; namely, that substance use treatment works and returns the economic investment through reductions in health care use and criminal justice and other costs at the societal level. Today's most relevant questions for intervention research focus on what works best from a comparative point of view, under what modifying or mediating conditions, and with what degree of cost-effectiveness. Aside from issues related to subpopulations, there are 5 broad contextual factors that the reader must consider when reading and distilling key learnings from the 2 reviews.'1,2 First, the charge given to the authors was to synthesize the treatment literature related to alcohol abuse and dependence and not other psychoactive drugs of abuse. While there is some legitimacy to the development of substance-specific interventions (for example, specific pharmacologic interventions for AUDs). the trend during the last several years in most specialized treatment settings has been toward a diverse, multiple drug-using client population. The treatment population has diversified to the extent that programs are often challenged to identify the primary drug of abuse. In Ontario, for example, the most recent data from the provincial Drug and Alcohol Treatment Information System (commonly referred to as DATIS) yield the following breakdown - alcohol only 33%. drug only 31%, and combined alcohol and drug 36% (excluding family members who are also seen as system clients in their own right). These data have held steady for the past several years. By implication, interventions need to be as adaptable as possible across substances, while recognizing the need for some specialization for pharmacological intervention. It also means that treatment research samples, even if they are cleaned to reduce contamination by substances other than the drug of primary interest for the research question, yield study results that may or may not apply in real-life, mixed drug-using treatment populations. Thus both efficacy trials and field effectiveness studies are needed to advance the substance use field. The second important factor to keep in mind in applying the results of these 2 important reviews is that, while they focus on the interventions, someone has to engage the client and deliver them with competence, respect, and empathy. The paper by Dr Martin and Dr Rehm2 notes the importance of therapist effectsp 352 and these effects are by no means trivial. In the adult and youth literature alike it is surprising how few clinically significant differences emerge in studies comparing the effectiveness of different interventions. The psychotherapy literature suggests that therapeutic alliance may account for as much as 30% of the variance in treatment outcome. …
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,043 | 0,084 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,006 | 0,005 |
| Bibliométrie | 0,007 | 0,004 |
| Études des sciences et des technologies | 0,001 | 0,006 |
| Communication savante | 0,008 | 0,009 |
| Science ouverte | 0,003 | 0,003 |
| Intégrité de la recherche | 0,008 | 0,009 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,014 | 0,002 |
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 source (Gemma direct ou Codex distillé), 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 ».