Crowdfunding for Complementary and Alternative Cancer Treatments in Tijuana, Mexico: Content Analysis
Bibliographic record
Abstract
BACKGROUND: Complementary and alternative (CAM) cancer treatment is often expensive and not covered by insurance. As a result, many people turn to crowdfunding to access this treatment. OBJECTIVE: The aim of this study is to identify the rationales of patients with cancer seeking CAM treatment abroad by looking specifically at crowdfunding campaigns to support CAM cancer treatment in Tijuana, Mexico. METHODS: We scraped the GoFundMe.com and GiveSendGo.com crowdfunding platforms for campaigns referencing CAM cancer clinics in Tijuana, initiated between January 1, 2022, and February 28, 2023. The authors created a coding framework to identify rationales for seeking CAM treatment in Tijuana. To supplement campaign metadata, we coded the beneficiary's cancer stage, type, age, specific treatment sought, whether the beneficiary died, gender, and race. RESULTS: Patients sought CAM cancer treatment in Tijuana because the (1) treatment offers the greatest efficacy (29.9%); (2) treatment offered domestically was not curative (23.2%); (3) the clinic treats the whole person, and addresses the spiritual dimension of the person (20.1%); (4) treatments are nontoxic, natural, or less invasive (18.2%); and (5) clinic offers the newest technology (8.5%). Campaigns raised US $5,275,268.37 and most campaign beneficiaries were women (69.7%) or White individuals (71.1%). CONCLUSIONS: These campaigns spread problematic misinformation about the likely efficacy of CAM treatments, funnel money and endorsements to CAM clinics in Tijuana, and leave many campaigners short of the money needed to pay for CAM treatments while costing beneficiaries and their loved one's time, privacy, and dignity. This study affirms that Tijuana, Mexico, is a very popular destination for CAM cancer treatment.
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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 | 0.008 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".