Best practices toolkit for family participation in <scp>ICU</scp> rounds
Bibliographic record
Abstract
RATIONALE, AIMS AND OBJECTIVES: Guidelines recommend inviting family members of intensive care unit (ICU) patients to rounds. We aimed to create a toolkit to support family participation in ICU bedside rounds, based upon evidence from research and in collaboration with ICU family member representatives and healthcare providers. METHODS: Ethnographic observations of rounds and interviews and focus groups with family members and ICU healthcare providers were analyzed for key themes, barriers and facilitators of participation, and suggestions. A full day workshop with family representatives and providers (physicians, nurses, social workers, and unit managers) from a diverse range of adult ICUs in Western Canada, including several community ICUs and a majority of large, urban ICUs enabled the collaborative development of key toolkit elements. RESULTS: We have developed an evidence-informed approach to patient-and-family-centered rounds that highlights the importance of six key elements foundational to patient and family centered rounds: Invitation, Orientation, Engagement, Summary, Questions, and Communication Follow-Up. We describe strategies, techniques, and templates to optimize these elements and interactions so that communication is more meaningful, and to facilitate the ability of family members to adopt a meaningful role as contributing members of the care team. CONCLUSION: There is consensus on general strategies for facilitating family participation in rounds and meaningful communication between family and the healthcare team during rounds as an important element of the continuum of communication in the ICU. The incorporation of these elements should be standardized, though tailored to user needs.
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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 | 0.055 | 0.093 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".