Insights from GoFundMe posts: Analyzing GoFundMe financial aid requests from brain tumor patients in Ontario, Canada
Bibliographic record
Abstract
Background: Primary central nervous system (CNS) tumors significantly affect individuals globally, with patients in Ontario, Canada, often bearing financial burdens for treatments such as oral chemotherapy due to insufficient coverage, resulting in complex insurance processes or out-of-pocket payments. However, limited understanding exists regarding other direct and indirect financial implications of their diagnosis. This study examines the financial strains, unmet needs, and overarching challenges encountered by Ontario's brain tumor patients, utilizing GoFundMe posts as a unique data source to explore additional financial costs linked to CNS tumor diagnoses in the region. Methods: A qualitative descriptive design employing thematic analysis analyzed GoFundMe posts supporting CNS tumor patients in Ontario from 2014 to 2021. A search strategy targeted posts featuring primary CNS tumor keywords, with NVivo 10 software facilitating post organization and coding. Results: Focused on Ontario, the study yielded a final dataset of 154 posts from an initial pool of 9025, revealing further financial strain due to income loss among patients and caregivers. Posts highlighted various concerns: (1) navigating the complexities of accessing support services, (2) worries about family's long-term financial and overall well-being, (3) insufficient public awareness about the financial and emotional burden on those affected, and (4) seeking emotional support, hope, and encouragement from the community. Conclusions: These GoFundMe posts highlight a connection between financial burden, emotional distress, and the need for improved access to financial and emotional support services. The results emphasize distinct financial challenges faced by CNS tumor patients within Ontario's healthcare system.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".