A Cross-sectional Comparison of Utilization of Medical Crowdfunding for Global Health
Bibliographic record
Abstract
Abstract The online solicitation of public donations has become an important financing option for health care expenses, intensified by increasing costs and deficits of universal public systems (1). With growing internet access and success of the largest social crowdfunding platform, GoFundMe, online appeals for medical causes have grown significantly over the last decade in low-to high-income countries. The purpose of this study was to qualitatively describe the use of GoFundMe as a crowdfunding platform for global health initiatives given its supremacy in the social crowdfunding market. Three different cohorts (n=100 each) of online solicitation were examined as a cross-section comparing global health appeals to those for personal health care and animal activism. Variables included the purpose for crowdfunding, the characteristics of beneficiaries and campaigns, and the factors associated with funding success. Our cross-sectional review found that global health campaigns were focused on voluntourism opportunities compared to more specific, individualized appeals for those in need. Global health campaigns appeared to be the least ambitious and generally the least successful of those reviewed. Grouping the most and least successful campaigns between the different cohorts, global health appears to be more successful when targeting a larger population to donate smaller amounts of money and relying on sharing via social media. We suggest that compared to online solicitation for personal health and animal activism objectives, crowdfunding on GoFundMe has unrealized potential as a tool for global health initiatives. More work should be conducted using different crowdfunding platforms and a more longitudinal review in order to expand on these findings and their implications on health care provision in the countries examined. Furthermore, future inquiry is needed to understand the social and ethical implications of online solicitation for global health endeavors in order to inform policy and promote discussion around equity and accessibility.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".