Controlled administration of cannabis to mitigate cannabis-attributable harm among recreational users: a quasi-experimental study in Germany
Bibliographic record
Abstract
Background: New approaches are required to slow down or reverse increasing trends of levels of delta-9-tetrahydrocannabinol (THC) and cannabis-attributable hospitalizations in Germany. Legal access to cannabis may constitute one viable effective policy response; however, available evidence does not suffice to inform a regulation model for Germany. The proposed study aims to reduce harm for cannabis users through legal access to herbal cannabis through pharmacies. Protocol: A quasi-experimental study comparing cannabis users with legal access to herbal cannabis (Berlin, intervention group) to those without legal access (Hamburg, control group) (total N=698). As the primary outcome, we hypothesize that: 1) illegal THC consumption will reduce by at least 50% in the intervention group and 2) total THC exposure in the intervention group will be reduced by at least 10% lower than that of the control group, taking into account baseline values. Secondary outcomes comprise measures of frequency of use, THC-impaired driving, and mode of administration. Paired t-tests and multilevel regression models will be performed for statistical analyses. Discussion: This study proposal is currently being reviewed by the ‘Federal Institute for Drugs and Medical Devices’ – the body responsible for approving research studies on classified substances, including cannabis. Upon approval and prior to the start of the study, a full ethical review will be undertaken. Results may inform a regulation model for Germany and other jurisdictions and are expected to deepen the understanding of the effects of legal access to cannabis. Pre-registration: German Clinical Trials Register (DRKS), DRKS00020829
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".