Crowdfunding abortion: an exploratory thematic analysis of fundraising for a stigmatized medical procedure
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,003 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».