Systematic review of evidence on the impact of nursing workload and staffing on establishing healthy work environments
Notice bibliographique
Résumé
Background This systematic review set out to examine the impact, if any, of nursing workload and staffing on creating and maintaining healthy work environments. For the purposes of this review, the term ‘healthy work environment’ was defined as ‘… a practice setting that maximizes the health and well-being of nurses, quality patient outcomes and organizational performance’. This definition identifies nurse, patient and organisational outcomes as indicators of the establishment and maintenance of a healthy work environment. Objectives The review sought to determine the impact of: Patient characteristics, nurse characteristics, system characteristics and system processes on workload, scheduling and concepts of productivity and utilisation Workload, scheduling and concepts of productivity and utilisation on the quality of outcomes for clients, nurses and the system/organisation Search strategy The search strategy sought to find both published and unpublished studies and papers written in the English language. A three-step search strategy approach was used. An initial limited search of MEDLINE and CINAHL databases was undertaken to identify optimal search terms followed by an analysis of the text words contained in the title and abstract, and of the index terms used to describe the article. A second extensive search using all identified keywords and index terms was then undertaken. The third step consisted of a search of the reference lists of all identified reports and articles for additional studies. Selection criteria Types of studies: This review considered research papers that addressed the appropriateness and effectiveness of workload and staffing concepts in fostering a healthy work environment in healthcare. The types of papers to be considered included: meta-analysis, randomised controlled trials, quasi-randomised controlled trials, cohort studies, case-control studies, descriptive studies and correlational studies. Types of participants: The review considered all participants involved or affected by workload and staffing concepts within the nursing workforce in a healthcare environment, including staff and patients. System and policy issues were also considered. Types of interventions: All workload and staffing strategies that impact on the work environment, patient and nurse outcomes were considered in this review. Types of outcome measures: Outcomes of interest were categorised into four groups: nursing staff outcomes, patient outcomes, organisational outcomes and system outcomes. Data collection and analysis Following assessment of methodological quality, data were extracted using data extraction tools based on the work of the Cochrane Collaboration and the Centre for Reviews and Dissemination. Statistical pooling was not possible and findings were presented in narrative form. Results Of the 2162 papers identified in the search, 171 were selected for full paper retrieval and assessed independently by two reviewers for methodological quality. A total of 40 papers were included in the review: one systematic review; one cohort study; and 38 correlational descriptive studies. Results were summarised in narrative form. The evidence suggests strong correlations between patient characteristics and work environments; and workload and staffing and the quality of outcomes for clients, nurses and the system/organisation. This gave rise to a number of recommendations for practice and for further research, such as: A greater proportion of regulated staffing (i.e. registered nurses, enrolled nurses, practical or vocational nurses) is associated with improved outcomes related to the Functional Independence Measure score, the Short Form Health Survey (SF-36) vitality score, patient satisfaction with nursing care, patient adverse events (including atelectasis, decubitus ulcers, falls, pneumonia, postsurgical and treatment infection and urinary tract infections) An increase in the number of registered nurse hours available is associated with improved patient outcomes in relation to falls, pneumonia, pressure ulcers, urinary tract infection, length of stay and postoperative infection rates
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 distillée sur la base complète
Imitation des enseignantsNi 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.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,012 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,002 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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.
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 tête enseignante, 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 ».