Crowdfunding abortion: an exploratory thematic analysis of fundraising for a stigmatized medical procedure
Bibliographic record
Abstract
BACKGROUND: Medical crowdfunding is the process of using a crowdfunding platform to raise funds for medical treatment and associated expenses, such as missing work or transportation costs to access care. This type of crowdfunding has become increasingly popular, and is an effective tool to raise financing for medical treatment in the absence of insurance. However, it is accompanied by questions of which diseases or treatments are viewed as worthy to fund and which do not fit the criteria of worthiness. In the context of an abortion, a legitimate and important medical procedure, there is a lack of research that determines if campaigners can successfully utilize GoFundMe to pay for abortions and abortion related services and costs given the social stigma around this procedure. Here, we explore the outcomes of crowdfunding campaigns for stigmatized needs and conditions by examining campaigns related to abortion. METHODS: A total of 211 campaigns that utilized the term "abortion" were retrieved on the medical-section of the GoFundMe crowdfunding platform. These results were thematically analyzed by each author and two distinctive categories were identified to group the campaigns. RESULTS: The categories of campaigns using the term "abortion" were: campaigns seeking funds to access abortion related services (n = 84) and campaigns using the choice not to terminate pregnancy or the harms of abortion as a reason to give (n = 127). The number of donors, number of Facebook shares, campaign location, funding requested, funding pledged, campaign creation date, relation between the recipient and campaigner, and proposed use for the funds were recorded for each included campaign. CONCLUSIONS: This study suggests that certain conditions or diseases may be less successful in medical crowdfunding based on perceived features of worthiness, such as in the case of abortion. In the categories we identified, campaigns seeking funds to access abortion-related services were less successful than campaigns using choosing not to terminate a pregnancy or the harms of abortion as a reason to give. This is an area of concern in medical crowdfunding - that certain medical needs will not be funded equitably.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".