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Record W3092626262 · doi:10.1093/eurpub/ckaa165.244

Crowdfunding for medical expenses in the United Kingdom

2020· article· en· W3092626262 on OpenAlexaboutno aff
Isabel Pifarré Coutrot, Laura Cornelsen, Richard Smith

Bibliographic record

VenueEuropean Journal of Public Health · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicFinTech, Crowdfunding, Digital Finance
Canadian institutionsnot available
Fundersnot available
KeywordsHealth careQuarter (Canadian coin)BusinessEmpowermentMedicineSample (material)Public relationsFamily medicineEconomic growthPolitical scienceEconomics

Abstract

fetched live from OpenAlex

Abstract Background Medical crowdfunding is a rapidly growing phenomenon worldwide and a new means for patients to finance health related expenses. It raises many ethical concerns such as increasing inequities. In the UK, which operates a state-funded universal health care system, the phenomenon is unexpected and hasn't yet been studied. Our study aims at answering basic questions as who is using crowdfunding to fund health-related expenses, for what and why. Methods We have drawn the first 400 medical campaigns amongst 1,000 available on GoFundMe UK website. We used a content analysis method to extract data from narratives on age, sex, health condition and funds' purposes. Results Among the 400 campaigns, 1/2 requested funds for cancer care for both common and rare cancers, from which 1/3 disclosed a stage 4. A third of the sample sought funds to get treatment abroad, mostly in Germany and the US, for most part cancer therapies such as immunotherapy but also alternative therapies. A quarter of the sample sought support to alleviate financial burden associated with ill-health. Other purposes included getting private care in the UK (19%) and getting medical equipment (18%). Conclusions Our findings may put forward some gaps within the National Health Service (NHS) and social care such as issues to access therapies or equipment, lack of holistic care and inadequate welfare patient support. However, it does not explain fully the rise of crowdfunding that may also be a counterpart of patients' empowerment. For instance, patients can shift to the private sector, in the UK or abroad, when a treatment is not available within the NHS, such as high cost last-resort treatments for those with poor prognosis. We recommend policy makers to: use medical crowdfunding to inform policy,support patients to make empowered decisions,protect patients from commercial traps. Key messages Studying medical crowdfunding allows better understanding of patients’ perceived or actual unmet need for health and social care to inform policy development. This threat to equity should be addressed globally by providing patients with support to be empowered, with universal health coverage and by regulating better private facilities and health tourism.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.012
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.599
Threshold uncertainty score0.700

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0120.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.218
GPT teacher head0.325
Teacher spread0.107 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2020
Admission routes1
Has abstractyes

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