Qualitatively exploring the intersection of health and housing needs in Canadian crowdfunding campaigns
Bibliographic record
Abstract
BACKGROUND: Online crowdfunding platforms such as GoFundMe fundraise millions of dollars annually for campaigners. Medical crowdfunding is a very popular campaign type, with campaigners often requesting funds to cover basic health and medical care needs. Here we explore the ways that health needs intersect with housing needs in Canadian crowdfunding campaigns. In Canada, both health and housing needs may be addressed through legislative or policy intervention, are public health priorities, and are perceived as entitlements related to people's basic human rights. We specifically develop a classification scheme of these intersections. METHODS: We extensively reviewed Canadian crowdfunding campaigns on GoFundMe, the largest charitable crowdfunding platform, using a series of keywords to form the basis of the classification scheme. Through this process we identified five categories of intersection. We extracted 100 campaigns, 20 for each category, to ascertain the scope of these categories. RESULTS: Five categories form the basis of the classification scheme: (1) instances of poor health creating the need to temporarily or permanently relocate to access care or treatment; (2) house modification funding requests to enhance mobility or otherwise meet some sort of health-related need; (3) campaigns posted by people with health needs who were not able to afford housing costs, which may be due to the cost of treatment or medication or the inability to work due to health status; (4) campaigns seeking funding to address dangerous or unhealthy housing that was negatively impacting health; and (5) people describing an ongoing cyclical relationship between health and housing need. CONCLUSIONS: This analysis demonstrates that health and housing needs intersect within the crowdfunding space. The findings reinforce the need to consider health and housing needs together as opposed to using a siloed approach to addressing these pressing social issues, while the classification scheme assist with articulating the breadth of what such co-consideration must include.
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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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".