Bibliographic record
Abstract
Background Bedside rounds (BR) have been proposed as an ideal method to promote patient-centred hospital care, but there is substantial variation in their implementation and effects. Our objectives were to describe the implementation of BR in hospital settings and determine their effect on patient-centred outcomes. Methods Data sources included Ovid MEDLINE, Ovid Embase, Scopus and Ovid Cochrane Central Registry of Clinical Trials from database inception through 28 July 2017. We included experimental studies comparing BR to another form of rounds in a hospital-based setting (ie, medical/surgical unit, intensive care unit (ICU)) and reporting a quantitative patient-reported or objectively measured clinical outcome. We used random effects models to calculate pooled Cohen’s d effect size estimates for the patient knowledge and patient experience outcome domains. Results Twenty-nine studies met inclusion criteria, including 20 from adult care (17 non-ICU, 3 ICU), and nine from paediatrics (5 non-ICU, 4 ICU), the majority of which (n=23) were conducted in the USA. Thirteen studies implemented BR with cointerventions as part of a ‘bundle’. Studies most commonly reported outcomes in the domains of patient experience (n=24) and patient knowledge (n=10). We found a small, statistically significant improvement in patient experience with BR (summary Cohen’s d=0.09, 95% CI 0.04 to 0.14, p<0.001, I2=56%), but no significant association between BR and patient knowledge (Cohen’s d=0.21, 95% CI −0.004 to –0.43, p=0.054, I2=92%). Risk of bias was moderate to high, with methodological limitations most often relating to selective reporting, low adherence rates and missing data. Conclusions BR have been implemented in a variety of hospital settings, often ‘bundled’ with cointerventions. However, BR have demonstrated limited effect on patient-centred 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 | 0.003 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.023 | 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".