Exploring the self-reported motivations of kratom (<i>Mitragyna speciosa</i> Korth.) use: a cross-sectional investigation
Bibliographic record
Abstract
Background Kratom (Mitragyna speciosa Korth.) use outside of Southeast Asia has increased over the past decade.Objectives This investigation clarifies kratom’s role in perceived well-being, overall health, and temporal correlation with drug use to understand kratom’s role in the self-treatment of substance use disorders (SUDs).Methods Between July 2019 and July 2020 an anonymous, cross-sectional, online survey was taken by 7,381 people who use kratom (PWUK) recruited through social media and other online resources. This included an assessment of (a) the relationship between self-reported overall health, concomitant use of drugs of misuse, and demographics; (b) the perceived effectiveness of kratom in self-treating diagnosed health conditions or symptoms; (c) the profile of PWUK primarily for drug dependence, pain, and mood or mental health conditions based on demographics.Results A total of 5,152 valid responses (45.9% females/53.7% males) were collected. Kratom was primarily used for self-treating pain (73.0%) and improving emotional or mental health conditions (42.2%) without clinical supervision. Those with a SUD (synthetic opioids, methadone, benzodiazepines, or heroin) used kratom after discontinuing illicit or other drugs (94.8%). The primary substances taken before or concomitantly with kratom were cannabis, cannabidiol, benzodiazepines, or kava. PWUKs report a dose-dependent benefit for alleviating pain and relieving negative moods. Adverse effects were primarily gastrointestinal, typically at high (>5 g/dose) and frequent (>22 doses/week) dosing.Conclusions Kratom was primarily used as a harm-reduction agent for SUDs and self-treatment of chronic conditions. Healthcare professionals need better information about kratom, its potential adverse effects, and clinically significant drug interactions.
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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 | 0.002 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".