Bibliographic record
Abstract
When people use online platforms to solicit funds from others for health-related needs, they are engaging in medical crowdfunding. This form of crowdfunding is growing in popularity, and its visibility is increasing as campaigns are commonly shared via social networking. A number of ethical issues have been raised about medical crowdfunding, one of which is that it introduces a number of privacy concerns. While campaigners are encouraged to share very personal details to encourage donations, the sharing of such details may result in privacy losses for the beneficiary. Here, we explore the ways in which privacy can be threatened through the practice of medical crowdfunding by exploring campaigns (n=100) for children with defined health needs scraped from the GoFundMe platform. We found specific privacy concerns related to the disclosure of private details about the beneficiary, the inclusion of images and the nature of the relationship between campaigner, funding recipient and beneficiary. For example, it was found that identifying personal and medical details about the beneficiary, including symptoms (n=52) and treatment history (n=43), were often mentioned by campaigners. While the privacy concerns identified are problematic, they are also difficult to remedy given the strong financial incentive to crowdfund. However, crowdfunding platforms can enhance privacy protections by, for example, requiring those campaigning on behalf of child beneficiaries to ensure consent has been obtained from their guardians and providing additional guidelines for the inclusion of personal information in campaigns made on behalf of those not able to give their consent to the campaign.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.037 | 0.112 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.023 |
| Scholarly communication | 0.013 | 0.019 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".