Engaging the Public to Identify Opportunities to Improve Critical Care: A Qualitative Analysis of an Open Community Forum
Bibliographic record
Abstract
OBJECTIVE: To engage the public to understand how to improve the care of critically ill patients. DESIGN: A qualitative content analysis of an open community forum (Café Scientifique). SETTING: Public venue in Calgary, Alberta, Canada. PARTICIPANTS: Members of the general public including patients, families of patients, health care providers, and members of the community at large. METHODS: A panel of researchers, decision-makers, and a family member led a Café Scientifique, an informal dialogue between the populace and experts, over three-hours to engage the public to understand how to improve the care of critically ill patients. Conventional qualitative content analysis was used to analyze the data. The inductive analysis occurred in three phases: coding, categorizing, and developing themes. RESULTS: Thirty-eight members of the public (former ICU patients, family members of patients, providers, community members) attended. Participants focused the discussion and provided concrete suggestions for improvement around communication (family as surrogate voice, timing of conversations, decision tools) and provider well-being and engagement, as opposed to medical interventions in critical care. CONCLUSIONS: Café participants believe patient and family centered care is important to ensure high-quality care in the ICU. A Café Scientifique is a valuable forum to engage the public to contribute to priority setting areas for research in critical care, as well as a platform to share lived experience. Research stakeholders including health care organizations, governments, and funding organizations should provide more opportunities for the public to engage in meaningful conversations about how to best improve healthcare.
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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 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.017 | 0.017 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.003 | 0.003 |
| 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".