Family participation in ICU rounds—Working toward improvement
Bibliographic record
Abstract
RATIONALE, AIMS, AND OBJECTIVES: Family participation in Intensive Care Unit (ICU) bedside rounds has been advocated as a way to improve communication between families and health care providers; however, the associated impact and modulators have not been fully described. The purpose of this study was to explore benefits, drawbacks, barriers, and facilitators to family participation in ICU rounds in order to inform ways to improve how families are integrated into rounds. METHODS: This was a qualitative exploratory study of ICU patients' family members (n = 29) and health care providers (n = 35) who work in ICU settings. Interviews and focus groups were conducted, and thematic analysis was used for data analysis. RESULTS: Benefits and drawbacks for families were related to knowledge and emotional impact and for health care providers were related to knowledge and transparency, with rapport as an additional benefit and logistical impact as a drawback. Barriers and facilitators during rounds and outside of rounds were identified, and suggestions for improvement included preparing and orienting families, summarizing, teaching modifications, follow-up, and organizational culture. CONCLUSIONS: Our study provides insight into the multiple processes involved in family participation in ICU rounds, along with suggestions for improvement. Our findings may help guide development of a structured approach to family participation in ICU rounds that can be adapted to local contexts.
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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 | 0.046 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".