Feasibility and Usability of a Web-Based Peer Support Network for Care Partners of People With Serious Illness (ConnectShareCare): Observational Study
Notice bibliographique
Résumé
Background: While it can be rewarding to provide care for a person with serious illness, care partners are often unprepared to manage and cope with the physical and emotional stresses that arise with disease progression and bereavement. Objective: We aimed to evaluate membership enrollment, engagement, and experiences within a web-based peer support network for active and bereaved care partners of people with serious illness. Methods: We conducted a formative evaluation of the ConnectShareCare peer-to-peer web-based support network, which targeted care partners of people with serious illness residing in the northeastern United States. Recruitment methods included marketing postcards, flyers, listserv messages, and referrals from community stakeholders, peers, and clinicians. Enrollment occurred through a self-guided, web-based process. Study participants included members enrolled in ConnectShareCare between April 2021 and June 2023. We used the network's analytics dashboard (eg, registration, usage, and notification logs) to evaluate membership enrollment and engagement in discussions. We used surveys of a subset of members to assess experiences, including satisfaction, ability to find meaning by supporting others, and value and opportunities for improvement. Results: Over 2 years, the network enrolled 250 members, with an average of 9 new members per month. Among 193 members providing information, most (58%, n=112) identified as active care partners, 17% (n=33) identified as bereaved care partners, and 27% (n=52) chose not to specify their role. Two-thirds of the 250 members did not post, 20% (n=50) posted 1-10 times, 6% (n=14) posted 11-25 times, 6% (n=15) posted 26-100 times, and 3% (n=7) posted more than 100 times. On average, 19 members posted per month resulting in 166 member posts per month. Moderators (1 community manager, 2 volunteer mentors, and 2 project team members) supported members with an average of 111 posts/month. In total, 187 discussion topics were created, including 42% (n=78) started by members and 58% (n=109) started by moderators. Seventy-eight discussion topics had 10 or more posts associated with them. The most frequent discussion topics focused on "check-ins" and "sources of joy and hope." Among 18 care partner members who completed a research survey, 69% (11/16) reported connecting with at least 1 person and 62% (10/16) reported that ConnectShareCare helped them find meaning and purpose by supporting others. Most reported satisfaction with support (12/16, 75%) and information (14/16, 88%) through the network. Although most noted that ConnectShareCare was easy to use (10/17, 59%), respondents were less likely to easily find the information they were seeking (6/16, 38%). Survey respondents found value in peer connection and support and identified opportunities to improve navigation of resources and engagement of members. Conclusions: Care partners of people with serious illness can use a web-based peer support network to find meaningful and useful support and information. Additional work is needed to identify the impact of the network on distress, social connectivity, and support programming.
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,015 | 0,039 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,003 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,001 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 source (Gemma direct ou Codex distillé), 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 ».