Inspiring ‘Gifts of Health’ : Exploring the use of patient stories in transmedia fundraising campaigns
Bibliographic record
Abstract
Patient stories speak to the often-daunting journey that patients embark on throughout their experience of their illness or injury. The emotions these narratives convey make it easy for audiences to connect with them, thus making them an important, versatile tool for fundraising for medical causes. Today, patient stories are commonly used by hospital foundations to fundraise, but are also used frequently for crowdfunding personal medical expenses, a trend that is surging in popularity with the rise of treatment costs. This project first examines patient stories using the theoretical lenses of motivations for health communication, personal and institutional fundraising, and narratology and transmedia storytelling in health communication. Using a sample of 10 patient stories collected from Canadian GoFundMe campaigns and hospital foundation websites, this MRP specifically seeks to identify key similarities and differences in the ways that private individuals and non-profit health institutions use patient stories for fundraising efforts. It then aims to identify tactics used to produce the most successful patient stories and fundraising campaigns. The three theoretical lenses will be used to create a specific coding framework through which the motivations of different authors will be determined. Each campaign’s images, text and interactive elements will be assessed to identify trends and tactics, which will then be compared with the campaigns’ overall financial and social successes. This project will extend fundraising and health communications theory by adding depth to the existing literature on crowdfunding for personal medical expenses. It will also help to integrate transmedia storytelling theory into the larger field of health communication by identifying the different ways that online communication platforms may be used to target and connect donors while increasing funds for medical campaigns. In addition, by providing a holistic analysis of each campaign’s content, paired with a preliminary effects analysis, this project contributes a range of practical implications for hospital foundations and individuals to use when crafting patient narratives for future campaigns.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".