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Enregistrement W4286794318 · doi:10.1002/14651858.cd007019.pub3

Hospital nurse-staffing models and patient- and staff-related outcomes

2019· review· en· W4286794318 sur OpenAlex

Pourquoi ce travail est dans la base

Une base qui oublie comment elle a trouvé un travail ne peut pas être vérifiée. Voici les voies qui ont admis celui-ci.

affAu moins un auteur déclare une institution canadienne dans l'instantané OpenAlex épinglé.
aboutLe titre ou le résumé porte un signal canadien du lexique géographique.

Notice bibliographique

RevueCochrane Database of Systematic Reviews · 2019
Typereview
Langueen
DomaineHealth Professions
ThématiqueNursing Roles and Practices
Établissements canadiensUniversity of British ColumbiaQueen's University
Organismes subventionnairesnon disponible
Mots-clésStaffingSkill mixMedicineWorkforcePsychological interventionMEDLINECase mix indexNursingWorkloadEmergency departmentHealth care

Résumé

récupéré en direct d'OpenAlex

Background: Nurses comprise the largest component of the health workforce worldwide and numerous models of workforce allocation and profile have been implemented. These include changes in skill mix, grade mix or qualification mix, staff‐allocation models, staffing levels, nursing shifts, or nurses’ work patterns. This is the first update of our review published in 2011. Objectives: The purpose of this review was to explore the effect of hospital nurse‐staffing models on patient and staff‐related outcomes in the hospital setting, specifically to identify which staffing model(s) are associated with: 1) better outcomes for patients, 2) better staff‐related outcomes, and, 3) the impact of staffing model(s) on cost outcomes. Search methods: CENTRAL, MEDLINE, Embase, two other databases and two trials registers were searched on 22 March 2018 together with reference checking, citation searching and contact with study authors to identify additional studies. Selection criteria: We included randomised trials, non‐randomised trials, controlled before‐after studies and interrupted‐time‐series or repeated‐measures studies of interventions relating to hospital nurse‐staffing models. Participants were patients and nursing staff working in hospital settings. We included any objective reported measure of patient‐, staff‐related, or economic outcome. The most important outcomes included in this review were: nursing‐staff turnover, patient mortality, patient readmissions, patient attendances at the emergency department (ED), length of stay, patients with pressure ulcers, and costs. Data collection and analysis: We worked independently in pairs to extract data from each potentially relevant study and to assess risk of bias and the certainty of the evidence. Main results: We included 19 studies, 17 of which were included in the analysis and eight of which we identified for this update. We identified four types of interventions relating to hospital nurse‐staffing models: introduction of advanced or specialist nurses to the nursing workforce; introduction of nursing assistive personnel to the hospital workforce; primary nursing; and staffing models. The studies were conducted in the USA, the Netherlands, UK, Australia, and Canada and included patients with cancer, asthma, diabetes and chronic illness, on medical, acute care, intensive care and long‐stay psychiatric units. The risk of bias across studies was high, with limitations mainly related to blinding of patients and personnel, allocation concealment, sequence generation, and blinding of outcome assessment. The addition of advanced or specialist nurses to hospital nurse staffing may lead to little or no difference in patient mortality (3 studies, 1358 participants). It is uncertain whether this intervention reduces patient readmissions (7 studies, 2995 participants), patient attendances at the ED (6 studies, 2274 participants), length of stay (3 studies, 907 participants), number of patients with pressure ulcers (1 study, 753 participants), or costs (3 studies, 617 participants), as we assessed the evidence for these outcomes as being of very low certainty. It is uncertain whether adding nursing assistive personnel to the hospital workforce reduces costs (1 study, 6769 participants), as we assessed the evidence for this outcome to be of very low certainty. It is uncertain whether primary nursing (3 studies, > 464 participants) or staffing models (1 study, 647 participants) reduces nursing‐staff turnover, or if primary nursing (2 studies, > 138 participants) reduces costs, as we assessed the evidence for these outcomes to be of very low certainty. Authors' conclusions: The findings of this review should be treated with caution due to the limited amount and quality of the published research that was included. We have most confidence in our finding that the introduction of advanced or specialist nurses may lead to little or no difference in one patient outcome (i.e. mortality) with greater uncertainty about other patient outcomes (i.e. readmissions, ED attendance, length of stay and pressure ulcer rates). The evidence is of insufficient certainty to draw conclusions about the effectiveness of other types of interventions, including new nurse‐staffing models and introduction of nursing assistive personnel, on patient, staff and cost outcomes. Although it has been seven years since the original review was published, the certainty of the evidence about hospital nurse staffing still remains very low.

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.

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,004
score de la tête « metaresearch » (Gemma)0,002
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Revue systématique · Signal consensuel: Revue systématique
GenreSignal candidat: Synthèse · Signal consensuel: Synthèse
Score de désaccord entre enseignants0,238
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0040,002
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0090,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,001
Science ouverte0,0000,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,155
Tête enseignante GPT0,465
Écart entre enseignants0,310 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle