SPCR-06 FINANCIAL AID REQUESTS BY BRAIN TUMOR PATIENTS IN ONTARIO THROUGH GOFUNDM
Bibliographic record
Abstract
Abstract OBJECTIVE/PURPOSE Ontario brain tumor patients sometimes suffer the burden of responsibility to pay for treatments like oral chemotherapy. However, less is known about other direct and indirect financial costs due to their diagnosis. The purpose of this study was to analyze publicly-available data from GoFundMe, an online fundraising platform, to explore the financial needs of brain tumor patients. METHOD A qualitative descriptive design drawing on thematic analysis was used to analyze GoFundMe requests to support individuals diagnosed with brain cancer in Ontario between 2014 and 2021. A coding framework was created to determine emerging themes using qualitative data analysis software NVivo 10. RESULTS There were 195 fundraising requests. Requests described financial strain from the loss of income experienced by the patient and their caregivers to afford cancer treatments. Requests highlighted 1) an overall lack of awareness of how and where to access financial aid and affordable psychosocial support; 2) concerns over the long-term financial well-being of bereaving family members; 3) A call for more public awareness of the financial burden and emotional distress experienced by of those impacted by brain cancer. CONCLUSIONS/CLINICAL IMPLICATIONS These GoFundMe requests highlight a connection between financial burden and emotional distress and an overall lack of awareness of where/how to seek financial and emotional support. These results will serve as the foundation to advocate for and raise awareness of the financial assistance needed by brain tumor patients and their families.
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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 | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".