Summary of the Best Evidence for Non‐Pharmacological Management of Sleep Disturbances in Intensive Care Unit Patients
Notice bibliographique
Résumé
AIM: To retrieve, evaluate and summarise the best evidence for non-pharmacological management of sleep disturbances in ICU patients, and to provide basis for clinical nursing practice. DESIGN: This study was an evidence summary followed by the evidence summary reporting standard of Fudan University Center for Evidence-based Nursing. METHODS: All evidence on non-pharmacological management of sleep disturbances in ICU patients from both domestic and international databases and relevant websites was systematically searched, including guidelines, expert consensuses, best practice, clinical decision-making, evidence summaries and systematic review. DATA SOURCES: UpToDate, BMJ Best Practice, Joanna Briggs Institute, Scottish Intercollegiate Guidelines Network, National Guideline Clearinghouse, National Institute for Health and Clinical Excellence, Yi Maitong Guidelines Network, Registered Nurses Association of Ontario, Canadian Medical Association: Clinical Practice Guideline, Guidelines International Network, WHO, the Cochrane Library, CINAHL, Embase, PubMed, Web of Science, CNKI, WanFang database, VIP database, SinoMed, The American Psychological Association, European Sleep Research Society, American Academy of Sleep Medicine and National Sleep Foundation were searched from the establishment of the databases to June 1, 2024. RESULTS: A total of 18 pieces of literature were included, involving 4 guidelines, 2 expert consensuses, 1 best practice and 11 systematic reviews. 25 pieces of evidence covering 4 categories of risk factors, sleep monitoring, non-pharmaceutical intervention, education and training were summarised. CONCLUSION: This study summarises the best evidence for non-pharmacological management of sleep disturbances in ICU patients. In clinical application, medical staff should make professional judgements and fully combine clinical situations and patient preferences to select evidence, laying a theoretical foundation for later empirical research to reduce the incidence of sleep disturbances in ICU patients and improve the sleep quality of critically ill patients. IMPLICATIONS FOR THE PROFESSION AND PATIENT CARE: Medical staff can refer to the best evidence to provide reasonable non-pharmacological management plans for sleep disturbances in ICU patients, improving their sleep quality and life satisfaction. IMPACT: The management of sleep disturbances in critically ill patients has not received sufficient attention and standardisation. This study summarises 25 pieces of the best evidence for non-pharmacological management of sleep disturbances in critically ill patients. Accurate and standardised evaluation and monitoring are the foundation of sleep management for ICU patients. This summary of evidence can help ICU nurses enhance their clinical practice. REPORTING METHOD: This evidence summary followed the evidence summary reporting specifications of Fudan University Center for Evidence-based Nursing, which were based on the methodological process for the summary of the evidence produced by the Joanna Briggs Institute. This study was based on the evidence summary reporting specifications of the Fudan University Center for the Evidence-based Nursing; the registration number is 'ES20231708'. PATIENT OR PUBLIC CONTRIBUTION: No Patient or Public Contribution.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,014 | 0,079 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,007 | 0,009 |
| Bibliométrie | 0,015 | 0,008 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,006 | 0,003 |
| Science ouverte | 0,003 | 0,002 |
| Intégrité de la recherche | 0,004 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,008 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».