Patients With Rare Diseases and the Power of Online Support Groups: Implications for the Medical Community
Notice bibliographique
Résumé
BACKGROUND: Previous studies have shown positive tangible health benefits from using online support communities for informational support, daily living support, and emotional support. The specifics of how these communities can be improved have not been studied in detail. OBJECTIVE: This study will investigate various sources of information that patients with genetic disorders use to learn more about their condition. We will be using patients with Ehlers-Danlos Syndrome (EDS) as a typical representation of the wider group of patients with genetic disorders. This study will also investigate the benefits and disadvantages of web-based platforms and how they can be improved. METHODS: We used quantitative and qualitative analyses in this study. We undertook a web-based questionnaire survey and semistructured qualitative interviews through Zoom. Questionnaire results were analyzed using descriptive analysis. Thematic coding with constant comparison was used for interview transcript analysis. RESULTS: A total of 436 respondents completed some or all of the survey. The majority of participants are female (386/413, 93.46%), and 24% (99/413) of them are in the age range of 25-34 years. Around 81% (336/413) of the participants have some type of college degree, and 55% (227/413) of them have graduate degrees. About 49.31% (204/413) of them are not currently employed. Most patients stated that their health care providers did not give accurate and complete information to them regarding their health situation (mean 2.87, SD 1.34). Also, patients perceived their providers as not knowledgeable regarding web-based communities that discuss patients' conditions (mean 1.93, SD 1.15). Patients are confident in using health care resources available in web-based health communities (mean 3.78, SD 1.13). We interviewed 30 participants. The demographics of the interviewees were aligned with those of the survey participants. A total of 9 different themes were identified based on the Qualtrics survey and qualitative interviews. Participants shared the pros and cons of different online support groups that they were using and gave suggestions for improvement. They requested a centralized database with different categories of resources classified based on different diseases. They also emphasized the importance of search features and the ability to find relevant information with a hashtag. Furthermore, they elaborated on the privacy and security concerns they have regarding web-based support group platforms. CONCLUSIONS: Patients with rare diseases are finding information not available from their health care providers in community support groups. The medical community and web developers have a great opportunity to help these people by engaging with their web-based communities.
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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 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| 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 ».