Crowdfunding FOR MEDICAL CARE: <i>Ethical Issues in an Emerging Health Care Funding Practice</i>
Bibliographic record
Abstract
Crowdfunding websites allow users to post a public appeal for funding for a range of activities, including adoption, travel, research, participation in sports, and many others. One common form of crowdfunding is for expenses related to medical care. Medical crowdfunding appeals serve as a means of addressing gaps in medical and employment insurance, both in countries without universal health insurance, like the United States, and countries with universal coverage limited to essential medical needs, like Canada. For example, as of 2012, the website Gofundme had been used to raise a total of 8.8 million dollars (U.S.) for seventy-six hundred campaigns, the majority of which were health related. This money can make an important difference in the lives of crowdfunding users, as the costs of unexpected or uninsured medical needs can be staggering. In this article, I offer an overview of the benefits of medical crowdfunding websites and the ethical concerns they raise. I argue that medical crowdfunding is a symptom and cause of, rather than a solution to, health system injustices and that policy-makers should work to address the injustices motivating the use of crowdfunding sites for essential medical services. Despite the sites' ethical problems, individual users and donors need not refrain from using them, but they bear a political responsibility to address the inequities encouraged by these sites. I conclude by suggesting some responses to these concerns and future directions for research.
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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 | 0.099 | 0.139 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.020 | 0.051 |
| Scholarly communication | 0.032 | 0.034 |
| Open science | 0.005 | 0.018 |
| Research integrity | 0.041 | 0.047 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".