Feasibility and Usability of a Web-Based Peer Support Network for Care Partners of People With Serious Illness (ConnectShareCare): Observational Study
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".