Providing Evidence-Based Care, Day and Night: A Quality Improvement Initiative to Improve Intensive Care Unit Patient Sleep Quality
Bibliographic record
Abstract
OBJECTIVE: Evidence-based guidelines recommend promoting sleep in the Intensive Care Unit (ICU), yet many patients experience poor sleep quality. We sought to engage allied health staff and patient families to determine barriers to sleep promotion, to measure sleep quality for ICU patients, and to evaluate the improvement in sleep quality after implementation of nursing morning report protocol and a doorway poster. DESIGN: The study followed an interrupted time-series framework of quality improvement. Qualitative diagnostics included focus groups and interviews with patients, families, and allied health care workers, analyzed by qualitative descriptive analysis. Quantitative diagnostics included direct observation of nurses and patients overnight. Analysis of primary outcome data used statistical process control methodology. PATIENTS: Patients included were >18 years old, admitted overnight to a Canadian tertiary academic ICU, with a Richards Agitation Sedation Scale (RASS) ≥-2. INTERVENTIONS: Sleep quality was measured using the Richards Campbell Sleep Questionnaire (RCSQ). Two interventions were developed: sleep quality in morning nursing report, and a doorway poster. MAIN RESULTS: A total of 2332 patient nights across 7 consecutive months were included for analysis. Baseline sleep in the ICU was poor (mean RCSQ 53.7/100). Root cause-analysis identified the most prominent sleep barriers as nurse stigma associated with less active management of patients and lack of physician engagement. No significant improvement occurred over the sleep quality improvement initiative (mean RCSQ 59.5/100). Sleep quality was better among non-delirious patients compared with delirious patients (mean RCSQ 62.7 vs 53.3). CONCLUSION: The intervention of a nursing morning report protocol and sleep posters did not improve the quality of ICU patient sleep in this study. Structured interviews revealed potential sleep barriers to be addressed such as nursing stigma and inappropriate awakenings. Nursing stigma has not been previously linked to sleep quality.
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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 | 0.086 | 0.093 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".