The experience of family caregivers of ventilator-assisted individuals who participated in a pilot web-based peer support program: A qualitative study
Bibliographic record
Abstract
Introduction: Family caregivers play an important role supporting the day-to-day needs of ventilator-assisted individuals (VAIs) living at home. Peer-to-peer communication can help support these caregivers and help them sustain caregiving in the community. Online peer-support has been suggested as a way to help meet caregivers' support needs. Methods: A qualitative descriptive approach was used to elicit the perspectives of support received from caregivers who participated in a pilot web-based peer support program from October to December 2018. Data were collected through the transcripts of weekly online peer-to-peer group chats. Data were analyzed using an integration of thematic and framework analysis. Results: In total, eight caregivers and five peer mentors participated in the pilot. All five mentors and four of the caregivers participated in the weekly chats. We identified three themes, a) The experience of caregivers is characterized by unique challenges related to the complexity of VAI care including technology; b) Mentors and caregiver participants reciprocally share support; c) Despite hardships, there are things that make caregiving easier and joyful. Discussion: Our results add to the growing body of evidence pointing to the importance of online communities for supporting vulnerable caregivers. The reciprocal element of peer support, where trained mentors and untrained participants both benefit from support, can help sustain peer-support interventions. Despite the challenges of providing care to a VAI, there are facilitators that may help ease the caregiving experience and caregivers can benefit from ongoing support that is tailored to their needs along the caregiving trajectory.
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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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.006 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".