Seven ways to get a grip on facilitating bedside team rounding
Bibliographic record
Abstract
Although classically considered a cornerstone of inpatient care, rounding at patients' bedsides is increasingly being replaced by rounding in workrooms. Workroom rounds may provide a sense of efficiency and comfort, however bedside rounds have multiple benefits for patients, trainees, and staff physicians. Alongside its benefits, there are human and institutional challenges when incorporating bedside rounding. This article aims to draw on our own experience of implementing bedside rounding at Kingston Health Sciences Centre, to guide staff physicians and institutions on how to implement bedside rounding effectively while overcoming its challenges. The following seven tips provide a framework to avoid pitfalls when implementing bedside team rounding on inpatient services.
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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 | 0.059 | 0.125 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.023 | 0.020 |
| Scholarly communication | 0.023 | 0.021 |
| Open science | 0.008 | 0.029 |
| Research integrity | 0.016 | 0.029 |
| Insufficient payload (model declined to judge) | 0.013 | 0.006 |
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".