A Perspective on the Effectiveness of Interventions for Alcohol and other Substance Use Disorders
Bibliographic record
Abstract
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. …
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.043 | 0.084 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".