What are the informational pathways that shape people’s use of cannabidiol for medical purposes?
Bibliographic record
Abstract
BACKGROUND: Cannabidiol (CBD) is commonly used to manage symptoms in conditions and diseases for which there is limited clinical research for its application. How consumers arrive and decide to use CBD for medical treatment, despite lacking clinical evidence, is largely unknown. In this paper, we seek to identify the informational pathways through which consumers arrive at CBD for medical purposes. METHODS: GoFundMe.com campaigns fundraising to purchase CBD between June 2017 and May 2019 were collected using the Crowdfunding for Health Research Portal (CHRP). Product descriptions were thematically analyzed to determine pathways leading to incorporation of CBD into medical treatment. Campaign characteristics such as fundraising ask, funding received, location, campaign title, description, Facebook shares, and number of donors were recorded. Specific medical uses of CBD proposed in campaigns were tabulated. RESULTS: The study identified 164 crowdfunding campaigns primarily from the USA (n=159), with several from Canada (n=5). The campaigns requested $2,219,284.24 (median, $7000) and raised $610,612.87 (median, $1805) from 6825 donors (median, 26). Many campaigns asked for other treatments or illness-related costs not specific to CBD. The campaigns were shared 42,299 times on Facebook (median, 156 shares). Three informational pathways were identified leading to incorporation of CBD into medical treatment, which were self-directed research (n=149), recommendations from a trusted care provider (n=36), and/or experiential insights shared by someone associated with or influencing the crowdfunders personal network (n=30). The proposed uses of CBD were for cancer (n=96), seizure-inducing diseases/conditions (n=48), other/unspecified (n=6), joint/inflammatory diseases (n=6), mental health disorders (n=3), nervous system diseases (n=3), and autoimmune diseases (n=2). CONCLUSIONS: Our results suggest that consumers crowdfunding come to CBD through internally motivated reasons versus exposure to advertisements or other forms of marketing. Campaign beneficiaries generally had an unmet medical need that other forms of treatment were not satisfying. Then, through one or more of the informational pathways identified, CBD is considered a potential solution.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".