Cannabis for pain: a cross-sectional survey of the patient information quality on the Internet
Bibliographic record
Abstract
BACKGROUND: Cannabis has increasingly become an alternative treatment for chronic pain, however, there is evidence of concomitant negative health effects with its long-term usage. Patients contemplating cannabis use for pain relief commonly see information online but may not be able to identify trustworthy and accurate sources, therefore, it is imperative that healthcare practitioners play a role in assisting them in discerning the quality of information. The present study assesses the quality of web-based consumer health information available at the intersection of cannabis and pain. METHODS: A cross-sectional quality assessment of website information was conducted. Three countries were searched on Google: Canada, the Netherlands, and the USA. The first 3 pages of generated websites were used in each of the 9 searches. Eligible websites contained cannabis consumer health information for pain treatment. Only English-language websites were included. Encyclopedias (i.e. Wikipedia), forums, academic journals, general news websites, major e-commerce websites, websites not publicly available, books, and video platforms were excluded. Information presented on eligible websites were assessed using the DISCERN instrument. The DISCERN instrument consists of three sections, the first focusing on the reliability of the publication, the second investigating individual aspects of the publication, and the third providing an overall averaged score. RESULTS: Of 270 websites identified across searches, 216 were duplicates, and 18 were excluded based on eligibility criteria, resulting in 36 eligible websites. The average summed DISCERN score was 48.85 out of 75.00 (SD = 8.13), and the average overall score (question 16) was 3.10 out of 5.00 (SD = 0.62). These overall scores were calculated from combining the scores for questions 1 through 15 in the DISCERN instrument for each website. Websites selling cannabis products/services scored the lowest, while health portals scored the highest. CONCLUSION: These findings indicate that online cannabis consumer health information for the treatment/management of pain presents biases to readers. These biases included websites: (1) selectively citing studies that supported the benefits associated with cannabis use, while neglecting to mention those discussing its risks, and (2) promoting cannabis as "natural" with the implication that this equated to "safe". Healthcare providers should be involved in the guidance of patients' seeking and use of online information on this topic.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".