Impacts of patient advisory councils on recovery for sepsis survivors: a case study
Bibliographic record
Abstract
INTRODUCTION: Sepsis is a life-threatening condition with significant long-term impacts for survivors and their families. The known benefits of patient engagement have led to increased efforts globally to involve survivors in sepsis research. This study aimed to characterize the experiences of sepsis survivors and their families in patient advisory councils (PACs) for two Canadian sepsis research networks (Action on Sepsis and Sepsis Canada) and explore how PAC involvement supports long-term recovery. METHODS: This mixed-methods cross-sectional study consisted of a structured survey, ten interviews, and one focus group discussion. All current members of the Sepsis Canada and Action on Sepsis PACs (n = 29) were invited to participate. The results of the survey were analyzed descriptively and used to inform the development of the semi-structured interview guide. Qualitative data were analyzed using a thematic approach. RESULTS: Overall, 16 PAC members participated. The majority of participants were women and over 40 years old. Survey scores showed that most participants felt meaningfully engaged, while the qualitative findings highlighted how PACs supported recovery and fostered community connections between survivors, families, and researchers. Major themes included sepsis experience, recovery journey, characteristics of PACs, characteristics of PAC participation, and impacts of PAC involvement. CONCLUSION: Our findings demonstrate that PACs provide critical benefits that extend beyond feeling valued or appreciated for contributing to a specific project. These findings highlight the value of patient-oriented research in shaping evidence-based practices and policies and emphasize the need for trauma-informed approaches and improved post-sepsis care pathways to enhance recovery outcomes.
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.024 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| 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".