Peer support provider and recipients’ perspectives on compassion in virtual peer support stroke programs: “You can’t really be supportive without compassion”
Bibliographic record
Abstract
BACKGROUND: Peer support programs demonstrate numerous benefits, including emotional, instrumental, informational, and affirmational social support. Since the COVID-19 pandemic, many peer support stroke programs in Canada have been delivered virtually. Compassion must be consistently applied to build meaningful interactions, but the shift to virtual services may have changed the quality of interaction and compassion in virtual services. While compassion is recommended in health and social services to improve outcomes, satisfaction, and service quality, compassion in virtual peer support stroke programs remains understudied. We aimed to describe compassionate support in virtual peer support stroke programs from peer support providers' and recipients' perspectives. METHODS: This qualitative descriptive study was guided by Sinclair & colleagues' model of compassion. Peer support recipients or peer support providers participated in interviews transcribed and analyzed using a hybrid thematic analysis. RESULTS: Sixteen were peer support recipients, six were peer support providers, and two were both peer support providers and recipients. Participants agreed that compassion was essential in these programs. Participants perceived compassion to be a result of the virtues of compassionate facilitators (i.e., genuineness, passion, and empathy), relational space, and communication within the virtual peer support stroke program (e.g., sense of awareness or intuition of compassion, aspects of engaged peer support provision), virtuous response (e.g., knowing the person and actions that made the peer support recipient feel like a priority). Compassion was facilitated by listening and understanding peer support recipients' needs as they relate to stroke (i.e., seeking to understand peer support recipients and their needs), attending to peer support recipients' needs (e.g., timely actions to address their needs), and achieving compassion-related program outcomes (e.g., alleviating challenges and enhancing wellbeing). The absence of these components (e.g., lacking genuineness, passion and empathy) was a barrier to compassion in virtual peer support stroke programs. CONCLUSIONS: Study findings describe facilitators and barriers to perceived compassion in virtual peer support stroke programs and provide practical recommendations that can be adapted into programs to improve program quality.
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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 | 0.016 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.011 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".