Nurse‐led evidence‐based quality improvement programme to improve intensive care unit patient sleep quality
Bibliographic record
Abstract
BACKGROUND: Patients in the intensive care unit (ICU) suffer from significant sleep disturbances, which can negatively impact their healing and overall health. Nurses, as the primary caregivers, need to have expertise in sleep management to ensure better patient outcomes. Implementing nurse-led, evidence-based sleep protocols in ICUs is crucial. AIM: This study aimed to improve ICU patients' sleep quality by developing and implementing a nurse-led, evidence-based SLEEP Bundle, including Sleep initiative, Light control, Eye mask and earplugs usage, Environment noise cancellation and Provision of non-pharmacological (aromatherapy and music therapy) and pharmacological (dexmedetomidine and painkillers) support. METHODS: The Framework of Evidence-based Continuous Quality Improvement and the Ottawa Model of Research Use framework were used to guide the development, implementation and assessment of the SLEEP Bundle. A quasi-experimental study was conducted in a 12-bed surgical intensive care unit (SICU), assessing patient-perceived sleep quality, nurses' self-report knowledge, attitudes and actions regarding patient sleep conditions and nurses' adherence to the interventions. INTERVENTIONS: In order to successfully translate evidence into clinical practice, the protocol was crafted with significant nurse involvement, input in sleep promotion materials and a flexible continuing education component, which provided credits to encourage nurse participation. A sleep-aid kit, complete with non-pharmacological tools, and a system of regular quality control and feedback were integral to the clinical application of the protocol. RESULTS: The intervention significantly enhanced ICU patients' sleep quality, as evidenced by a significant increase in Richards-Campbell Sleep Questionnaire scores from 62 (IQR = 48-72) to 70 (IQR = 62-76) (95% CI [-10.000, -6.000], Z = -6.100, p < .001). Nurses demonstrated a 100% agreement in knowledge items and a significant upsurge in action items following the intervention. Concurrently, adherence to practice standards showed notable improvements in sleep management practices, including enhanced sleep quality assessment, daytime functional exercise support and compliance with environmental regulations, along with increased use of earplugs, eye masks and aromatherapy/music therapy. CONCLUSIONS: The study highlights the effectiveness and feasibility of a nurse-led sleep management strategy, as demonstrated by improved patient outcomes and increased nurse adherence to sleep promotion interventions. RELEVANCE TO CLINICAL PRACTICE: The significant improvements in sleep quality as well as the increased adherence to evidence-based interventions by nurses suggest that this SLEEP Bundle could be effectively translated to other clinical settings.
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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 | 0.040 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".