Reducing medical bankruptcy through crowdfunding: Evidence from giveforward
Bibliographic record
Abstract
An estimated 62% of individual bankruptcy filings in the United States were a direct result of costs borne from medical treatment following illness or injury. We consider the potential of online crowdfunding to alleviate the issue, wherein patients reach out to their social network for monetary support to help cover medical bills. We examine the effect of medical crowdfunding using proprietary data from one of the largest medical crowdfunding platforms, GiveForward.com, combined with state records of bankruptcy filing. Controlling for a variety of socioeconomic indicators, we find evidence that fundraising helped prevent between 114 and 136 bankruptcies across the US, per quarter, representing 3.9 percent of all medical related bankruptcies. Further, we explore the relationship between crowdfunding and public health insurance, finding evidence of a substitution effect when health insurance coverage is high. We discuss the implications of our findings for healthcare policy and crowdfunding.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.011 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".