Impacts of news media coverage on Canadian medical crowdfunding campaigns
Bibliographic record
Abstract
Abstract While donation‐based crowdfunding for health‐related purposes raises hundreds of millions of dollars yearly, most campaigns fail to meet their fundraising goals. Crowdfunding campaigners are advised to seek traditional news media coverage of their campaigns to increase donor interest and fundraising success. In this study, we seek to better understand what happens to donor behavior via donation‐based crowdfunding campaigns after they receive news media coverage. While research has focused on the impact of social media sharing on donation‐based crowdfunding, academic analyses of the impact of news media coverage is largely speculative. We searched the Newsstream and Factiva databases for Canadian news coverage of domestic donation‐based health‐related crowdfunding campaigns. This news coverage was paired with the crowdfunding campaign reported on in the story. Campaign text and daily fundraising totals and donor amounts were recorded for the 7 days before and after publication of the news article. The authors identified emergent patterns in this data around the amplification of personal information from the crowdfunding campaign to a wider audience and inclusion of new personal details. This process identified 17 relevant pairs of news stories and crowdfunding campaigns over a review period of just under 5 months in 2021–22. These campaigns raised a total of CAD$443,134 (median CAD$20,030) out of a total goal of CAD$772,500 (median CAD$40,000) or 57.4% of the requested funds. Median campaign donations and donor numbers increased for the 3 days following publication of the news article. Our exploratory analysis shows a relationship between crowdfunding campaigns that receive news media coverage and the numbers of donations and total amount donated shortly after this coverage. Campaigners may feel pressure to participate in news media coverage in order to reach their fundraising goals. Media coverage has implications for campaign recipient privacy and the equitable distribution of health‐related funding. This exploratory analysis establishes the need for additional research on this topic.
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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 | 0.006 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".