“…When it came to sensitive information, we made edits, and we took it back”: qualitatively exploring the role responsibilities taken on by Canadians who crowdfund on behalf of someone else from a privacy perspective
Bibliographic record
Abstract
BACKGROUND: Medical crowdfunding, a type of donation-based crowdfunding, is gaining prominence and enabling people to gather funds for medical treatments, surgeries, and other health needs. While this practice may democratize access to health care, it also raises ethical concerns, including breaching individuals’ privacy. Despite these concerns, little consideration has been given specifically to the privacy-related issues that emerge when people crowdfund on behalf of others’ health-related financial needs. METHODS: A study was undertaken to qualitatively explore the roles and associated role responsibilities of Canadians who crowdfund on behalf of others for their health needs. Twelve interviews were conducted with participants who had posted campaigns on the GoFundMe platform between January and December 2023. Interviews were transcribed, coded, and analyzed thematically. FINDINGS: Three key roles that had important privacy dimensions were identified: managing initial content, navigating informational considerations, and facilitating ongoing connections. Campaigners typically collaborated with recipients to craft compelling narratives, seek consent for sharing personal information, and provide regular updates to maintain donor engagement. Balancing campaign transparency with recipients’ privacy concerns was crucial in the crowdfunding process. CONCLUSION: Campaigners play pivotal roles in medical crowdfunding when doing so on behalf of funding recipients, including balancing the need for fundraising with the protection of recipients’ privacy. Clear guidelines are needed to support campaigners in navigating the ethical complexities that emerge. Further research is needed to address existing knowledge gaps and enhance the ethical integrity of crowdfunding practices.
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 | 0.010 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.031 | 0.020 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".