Patient Experiences With Prescription Cannabinoids in Germany: Protocol for a Mixed Methods, Exploratory, and Anonymous Web-Based Survey
Bibliographic record
Abstract
BACKGROUND: Medical cannabinoids are controversial. Their use is comparatively rare, but it is rising. Since 2017, cannabinoids can be prescribed in Germany for a broader range of indications. Patient surveys on these drugs are hampered by the stigmatization of cannabinoids and their (still) low prevalence in medical contexts. Against this background, patients' willingness to provide information is limited. Moreover, it is logistically challenging to reach them with a survey. A thorough knowledge of currently ongoing therapies and their effects and side effects, however, is important for a more appropriate and effective use of cannabinoids in the future. OBJECTIVE: This study is an exploratory data collection using a representative sample. The main goal is to provide a detailed picture of the current use of medical cannabinoids in Germany. It is intended to identify subgroups that may benefit particularly well or poorly. METHODS: We are conducting a representative, anonymous, cross-sectional, one-time, web-based survey based on mixed methods in 3 German federal states. Health conditions under cannabinoid therapy and before are documented with validated, symptom-specific questionnaires. This allows an estimation of the effect sizes of these therapies. The selection of parameters and questionnaires was based on the results of independent qualitative interviews in advance. Representative samples of the hard-to-reach study population are obtained by cluster sampling via contracted physicians of the statutory health insurance companies. RESULTS: Recruitment was ongoing until the end of June 2022, with 256 enrolled participants. Validated questionnaires on pain, spasticity, anorexia or wasting, multiple sclerosis, nausea or vomiting, depression, and attention deficit hyperactivity disorder (ADHD) were selected. Symptom scores are being assessed for both current conditions under cannabinoid therapy and conditions prior to this therapy (in retrospect). Validated questionnaires are also used for treatment satisfaction and general quality of life. These are supplemented by existing diagnoses, a detailed medication history, any previous experiences with cannabis or illegal substances, experiences with the prescription process, and sociodemographic data. Based on the results of the previous qualitative interviews, questions were added regarding prior experience with relaxation methods and psychotherapy, personal opinions about cannabinoids, pre-existing or symptom-related psychological trauma, and different experiences with different cannabis-based therapies. CONCLUSIONS: The exploratory mixed methods approach of this project is expected to provide valid and relevant data as a basis for future clinical research. The study design may be representative for a large proportion of outpatients treated with cannabinoids in the German federal states studied. It may have less bias toward social desirability and may provide valuable information in addition to existing studies. Due to the observational and cross-sectional nature of this study, various limitations apply. Causal relations cannot be drawn. TRIAL REGISTRATION: German Clinical Trials Register DRKS00023344; https://drks.de/search/en/trial/DRKS00023344. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/38814.
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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 | 0.025 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.004 |
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".