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Enregistrement W7125518349 · doi:10.70082/ah9py878

Analysis Of Nurse-To-Patient Ratios And Their Direct Influence On Inpatient Mortality And Nursing Burnout: A Healthcare Systems Study

2024· article· W7125518349 sur OpenAlexaboutno aff
Abdulaziz Khalid Hadi Almutairi, Najwa Mohammed Abdullah Zain, Aljazi Saad Amer Alqahtani, Alnoori Saad Amer Alqahtani, Alanood Mateb Kalawi Alharbi, Ashwaq Mohammed Hussain Alqhtani, Khalid Nammas Radah Albaqami, Layla Mohammed Muthhil Alotibi, Sarah Saad Saif Alqahtani, Fahad Ali O. Almutairi, Khulood Hamoud Hassan Almutairi, Nujud Falah Rabah Almutairi

Notice bibliographique

RevueThe Review of Diabetic Studies · 2024
Typearticle
Langue
DomaineNursing
ThématiqueNursing education and management
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésStaffingWorkforceIntervention (counseling)Health careAcute careWorkloadSkill mixPatient safetyBurnout

Résumé

récupéré en direct d'OpenAlex

Background: The contemporary healthcare ecosystem is currently navigating a precarious equilibrium between escalating patient acuity and constrained fiscal resources. This tension has precipitated a pervasive condition of Systemic Inpatient Vulnerability, characterized by an increased susceptibility of acute care patients to adverse events, including preventable mortality and failure to rescue (FTR). Concurrently, the nursing workforce—the primary surveillance system in acute care—is facing a global epidemic of occupational burnout, a syndrome of emotional exhaustion and depersonalization that compromises clinical vigilance. The prevalence of this dual burden is ubiquitous across Global Healthcare Systems, affecting patient outcomes in public and private sectors alike. The conventional management strategy, Intervention 2 (Standard/Variable Staffing), relies on flexible, budget-driven, or acuity-adjustable staffing models. While designed to optimize operational efficiency, this standard of care often lacks statutory floors, leading to chronic understaffing and significant variability in care delivery. In response, Intervention 1 (Mandated Nurse-to-Patient Ratios) has emerged as a promising alternative policy intervention. By legislating a maximum number of patients per nurse, this intervention aims to secure a minimum standard of clinical surveillance and mitigate workforce exhaustion. Objective: The primary objective of this systematic review is to systematically compare the effectiveness of Mandated Nurse-to-Patient Ratios (Intervention 1) versus Standard/Variable Staffing Models (Intervention 2) on key outcomes for Inpatients and Registered Nurses (Population). Specifically, this review aims to quantify the direct influence of these staffing paradigms on inpatient mortality and failure to rescue (primary patient outcomes) and nursing burnout and job dissatisfaction (primary workforce outcomes), thereby informing evidence-based policy in healthcare administration. Methods: This review was conducted in strict adherence to the PRISMA 2020 (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. A comprehensive search was executed across major bibliometric databases including MEDLINE, CINAHL, Cochrane Library, and Scopus, targeting peer-reviewed literature published between 2000 and 2024. The study selection was governed by the PICO framework: Population (Acute care inpatients and Registered Nurses); Intervention (Mandated/Minimum nurse-to-patient ratios); Comparison (Variable, budget-based, or non-mandated staffing); Outcomes (Mortality, FTR, Burnout, Job Dissatisfaction). Included studies encompassed randomized controlled trials (RCTs), prospective and retrospective cohort studies, and large-scale cross-sectional analyses. Quality assessment was rigorously performed using the Newcastle-Ottawa Scale (NOS) for observational studies to evaluate risk of bias in selection, comparability, and outcome ascertainment. Results: The systematic synthesis includes data from 85 primary studies 1, representing a massive cohort of over 288,000 nurses and millions of patient discharge records across more than 30 countries. The findings indicate a robust, dose-dependent relationship between staffing levels and outcomes. High-level analysis reveals that each additional patient assigned to a nurse's workload is associated with a 7% increase in the odds of 30-day inpatient mortality 2 and a concurrent 7% increase in failure-to-rescue rates.4 In jurisdictions where Intervention 1 was implemented, such as Queensland, Australia, post-implementation data showed 145 avoided deaths and 255 avoided readmissions within the first year.5 Regarding workforce outcomes, every additional patient per nurse is associated with a 23% increase in the odds of burnout and a 15% increase in job dissatisfaction.4 The review also identifies significant secondary benefits, including reductions in length of stay (LOS) and hospital costs, challenging the economic arguments against ratios. Conclusion: The comparative effectiveness analysis definitively favors Mandated Nurse-to-Patient Ratios over variable staffing models. The evidence demonstrates that mandated ratios function as a critical safety mechanism, significantly reducing preventable mortality and alleviating the profound burden of nursing burnout. The implications for clinical practice in Global Healthcare Systems suggest that staffing must be treated as a fixed clinical resource rather than a variable operational cost. Future research should focus on the economic modeling of ratio implementation in diverse payer systems and the integration of acuity metrics into statutory frameworks to further refine this intervention.

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 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)0,002
score de la tête « metaresearch » (Gemma)0,001
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: Qualitatif · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,403
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0020,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0020,000
Bibliométrie0,0010,002
Études des sciences et des technologies0,0000,001
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
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 Opus0,047
Tête enseignante GPT0,391
Écart entre enseignants0,344 · 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

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeQualitatif
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2024
Routes d'admission1
Résumé présentoui

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Même revueThe Review of Diabetic StudiesMême sujetNursing education and managementTravaux en français237 207