"Tremendous financial burden": Crowdfunding for organ transplantation costs in Canada
Bibliographic record
Abstract
Online crowdfunding platforms such as GoFundMe are used to raise funds for health-related expenses associated with medical conditions such as organ transplantation. By investigating crowdfunding in Canadian organ transplantation, this study aimed to increase understanding of the motivations and outcomes of organ transplantation crowdfunding. Canadian liver and kidney transplantation campaigns posted to GoFundMe between May 30 & 31 2018 were identified and after exclusion, 258 kidney and 171 liver campaigns were included in study. These campaigns were coded for: worthiness of the campaign recipient, requested financial and non-monetary contributions, how monetary donations would be spent, and comments on the Canadian health system, among others. Results suggest Canadian organ donors, transplant candidates, recipients, and their families and caregivers experience significant financial difficulties not addressed by the public health system. Living and medication costs, transportation and relocation expenses, and income loss were the expenses most commonly highlighted by campaigners. Liver campaigns raised nearly half their goal while kidney campaigns received 11.5% of their requested amount. Findings highlight disease burden and the use of crowdfunding as a response to the extraordinary costs associated with organ transplantation. Although crowdfunding reduces some financial burden, it does not do so equitably and raises ethical concerns.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".