Media portrayal of illness-related medical crowdfunding: A content analysis of newspaper articles in the United States and Canada
Bibliographic record
Abstract
BACKGROUND: Medical crowdfunding is a growing phenomenon, and newspapers are publishing on the topic. This research analyzed how illness-related crowdfunding and crowdfunding campaigns have recently been represented in newspapers that are popular in the United States and Canada. METHODS: A sample of 336 articles about medical crowdfunding published during the two year time period from October 7, 2015 to October 6, 2017 was produced using a Factiva search of the English language newspapers with the largest Canadian and United States readership. A coding frame was developed for and applied to the sample to analyze content. RESULTS: Articles portrayed crowdfunding campaigns positively (43.75%) and neutrally (47.92%), but rarely negatively (4.76%). Articles mostly mentioned the crowdfunding phenomenon with a neutral characterization (93.75%). Few (8.63%) articles mentioned ethical issues with the phenomenon of crowdfunding. Ailments most commonly precipitating the need for a campaign included cancer (49.11%) and rare disease (as stated by the article, 36.01%). Most articles (83.04%) note where donations and contributions can be made, and 59.23% included a hyperlink to an online crowdfunding campaign website. Some articles (26.49%) mentioned a specific monetary goal for the fundraising campaign. Of the 70 (20.83%) articles that indicated the treatment sought may be inefficacious, was unproven, was experimental or lacked regulatory approval, 56 (80.00%) noted where contributions can be made and 36 (51.43%) hyperlinked directly to an online crowdfunding campaign. CONCLUSIONS: Crowdfunding campaigns are portrayed positively much more often than negatively, many articles promote campaigns for unproven therapies, and links directly to crowdfunding campaign webpages are present in most articles. Overall, crowdfunding is often either implicitly or explicitly endorsed.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.016 | 0.021 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".