A CROSS‐CUTTING RESEARCH AGENDA ON ALCOHOL, TOBACCO AND OTHER DRUGS: WHERE TO START?
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 teacher head, 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".