Family Caregivers of Individuals With Neuromuscular Disease Participating in a Randomized Controlled Trial of a Digital Peer Support Program: Nested Qualitative Study
Bibliographic record
Abstract
Background: Family caregivers have primary responsibility for providing care in the home for people with neuromuscular diseases (NMDs). This may negatively affect caregiver health. Peer support may enhance quality of life and reduce stress among family caregivers, but few trials have been conducted in NMD caregivers. Therefore, we conducted a randomized controlled trial with a nested qualitative evaluation (this report) of a 12-week digital peer support intervention for family caregivers of children and adults with NMD. Objective: The aim of the study is to gain insights into the perspectives of intervention participants and peer mentors regarding their experiences with the trial's digital peer support program. Methods: We conducted a nested exploratory qualitative study (August 2022 to March 2024), recruiting participants who were randomized to the intervention arm of the randomized controlled trial and study mentors. We conducted semistructured interviews via videoconferencing. Homophily theory and the theoretical framework of acceptability informed our analyses. Results: We interviewed 21 participants and 10 mentors, identifying four themes: (1) program participation motivators, (2) program expectations and appreciation, (3) program appropriateness, and (4) the peer mentor-mentee dyad. We found that participants were motivated to join the program due to existing caregiver burden and social isolation. Participants and mentors appreciated the program's sense of community and flexible digital format, with participants valuing emotional and informational support. However, challenges in relating to each other's situations due to participant and mentor heterogeneity in the extent of the care recipient's needs were perceived to limit benefit. Conclusions: Peer support was perceived as potentially beneficial in relieving caregiver burden and social isolation, creating a sense of community that provides emotional and informational support. The digital and flexible format was an important facilitator. An important barrier was participant-mentor heterogeneity resulting in reduced perception of homophily. These findings can inform the development of other digital peer support programs to alleviate caregiver burden and isolation and provide emotional relief and informational guidance.
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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 | 0.042 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".