Lack of Diversity in Research on Females with Ehlers-Danlos Syndromes: Recruitment Protocol for a Quantitative Online Survey
Notice bibliographique
Résumé
BACKGROUND: Ehlers-Danlos syndromes (EDS) are a group of connective tissue disorders caused by fragile lax collagen. Current EDS research lacks racial and ethnic diversity. The lack of diversity may be associated with the complexities of conducting a large international study on an underdiagnosed condition and a lack of EDS health care providers who diagnose and conduct research outside of the United States and Europe. Social media may be the key to recruiting a large diverse EDS sample. However, studies that have used social media to recruit have not been able to recruit diverse samples. OBJECTIVE: This study aims to discuss challenges, strategies, outcomes, and lessons learned from using social media to recruit a large sample of females with EDS. METHODS: Recruitment on social media for a cross-sectional survey examining dyspareunia (painful sexual intercourse) in females was examined. Inclusion criteria were (1) older than 18 years of age, (2) assigned female at birth, and (3) diagnosed with EDS. Recruitment took place on Facebook and Twitter (now X), from June 1 to June 25, 2019. RESULTS: A total of 1178 females with EDS were recruited from Facebook (n=1174) and X (n=4). On Facebook, participants were recruited via support groups. A total of 166 EDS support groups were identified, 104 permitted the principal investigator to join, 90 approved posting, and the survey was posted in 54 groups. Among them, 30 of the support groups posted in were globally focused and not tied to any specific country or region, 21 were for people in the United States, and 3 were for people outside of the United States. Recruitment materials were posted on X with the hashtag #EDS. A total of 1599 people accessed the survey and 1178 people were eligible and consented. The average age of participants was 38.6 (SD 11.7) years. Participants were predominantly White (n=1063, 93%) and non-Hispanic (n=1046, 92%). Participants were recruited from 29 countries, with 900 (79%) from the United States and 124 (11%) from Great Britain. CONCLUSIONS: Our recruitment method was successful at recruiting a large sample. The sample was predominantly White and from North America and Europe. More research needs to be conducted on how to recruit a diverse sample. Areas to investigate may include connecting with more support groups from outside the United States and Europe, researching which platforms are popular in different countries, and translating study materials into different languages. A larger obstacle to recruiting diverse samples may be the lack of health care providers that diagnose EDS outside the United States and Europe, making the pool of potential participants small. There needs to be more health care providers that diagnose and treat EDS in countries that are predominantly made up of people of color as well as research that specifically focuses on these populations. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR1-10.2196/53646.
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 | 0,008 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| 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 ».