Assessing the quality of information provided on websites selling Kratom (Mitragyna speciosa) to consumers in Canada
Bibliographic record
Abstract
BACKGROUND: Amid a Canadian opioid crisis, many have turned to natural health products, such as kratom (Mitragyna speciosa), to manage their opioid withdrawal. Kratom has also been reported to relieve anxiety, improve stamina, and heighten physical performance. Given that kratom is not authorized for sale by Health Canada, many have turned to online retailers to purchase kratom due to its easy accessibility online. This study investigated the quality of consumer health information provided on the websites of online vendors selling kratom to consumers in Canada. METHODS: Following searches on Google.ca using search terms designed to simulate the information-seeking behaviour of a typical patient-user online, eligible websites were assessed using the 16-question DISCERN instrument, a tool designed to assess the quality of consumer health information. Searches were conducted on March 27, 2020 and only websites presenting information in English were included. RESULTS: A total of 200 webpages were identified; after screening based on eligibility criteria and combining different webpages that belonged to the same website, 51 websites were found to be eligible. The mean summed DISCERN score across all 51 websites was 36.95 (SD = 2.44) out of 75, which reflects poor quality consumer health information across the subset of websites. The overall quality of websites was poor, as 78% (n = 40) of vendors received a score of 2 or less out of 5. CONCLUSIONS: Individuals who seek information about kratom online are frequently exposed to poor quality consumer health information. Those looking to purchase kratom online are not provided with the critical information necessary to make an informed decision regarding its use, such as the complete details about the risks and side effects or a description of how kratom affects the body. Given the growing interest in kratom, knowledge of the quality of information available can lead to improved dialogue between healthcare providers and patients.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".