Ranking the Harm of Psychoactive Drugs Including Prescription Analgesics to Users and Others–A Perspective of German Addiction Medicine Experts
Bibliographic record
Abstract
Background Over the past 15 years, comparative assessments of psychoactive substance harms to both users and others have been compiled by addiction experts. None of these rankings however have included synthetic cannabinioids or non-opioid prescription analgesics (NOAs, e.g. gabapentinoids) despite evidence of increasing recreational use. We present here an updated assessment by German addiction medicine experts considering changing Western consumption trends including those of NOAs. Methods In a first survey, 101 German addiction medicine physicians evaluated the health and social harms (in 5 dimensions) of 33 psychoactive substances including opioid and NOAs, to both users and others. In a second survey, 36 addiction medicine physicians estimated the relative weight of each health and social harm dimension to determine the overall harm rank of an individual substance. We compared our ranking with the most recent European assessment from 2014. Results Illicit drugs such as methamphetamine, heroin, cocaine and also alcohol were judged particularly harmful, and new psychoactive drugs (cathinones, synthetic cannabinoids) were ranked among the most harmful substances. Cannabis was ranked in the midrange, on par with benzodiazepines and ketamine – somewhat more favorable compared to the last European survey. Prescribed drugs including opioids (in contrast to the USA, Canada and Australia) were judged less harmful. NOAs were at the bottom end of the ranking. Conclusion In Germany, alcohol and illicit drugs (including new psychoactive substances) continue to rank among the most harmful addictive substances in contrast to prescribed agents including opioid analgesics and NOAs. Current laws are incongruent with these harm rankings. This study is a pioneer in rating the harms of both together, illicit and licit (prescription) psychoactive substances.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".