TY - JOUR
T1 - Factors That Influence Nurse Staffing Levels in Acute Care Hospital Settings
AU - Porcel-Gálvez, Ana María
AU - Fernández-García, Elena
AU - Rafferty, Anne Marie
AU - Gil-García, Eugenia
AU - Romero-Sánchez, José Manuel
AU - Barrientos-Trigo, Sergio
N1 - Funding Information:
Grant support was received from the Regional Health Ministry of Andalusia (PI 0828/2012).
Funding Information:
The authors would like to thank Trevor Mullers, BSc, MSc, Statistician and Professor at Florence Nightingale Faculty of Nursing, Midwifery and Palliative Care in King's College London, for his contribution in the review of the data analysis. We also thank the nursing staff of the Andalusian Health Care System and the INICIARE Research Team for their contribution to this research. The ethics committee of the Andalusian Healthcare System approved the project with the ethical approval number 1967. Grant support was received from the Regional Health Ministry of Andalusia (PI 0828/2012). Clinical Resources International Council of Nurses. Nursing Now. https://www.nursingnow.org RN4CAST. Nursing forecasting in Europe. http://www.rn4cast.eu World Health Organization. Nursing Now Campaign. https://www.who.int/hrh/news/2018/nursing_now_campaign/en/ International Council of Nurses. Nursing Now. https://www.nursingnow.org RN4CAST. Nursing forecasting in Europe. http://www.rn4cast.eu World Health Organization. Nursing Now Campaign. https://www.who.int/hrh/news/2018/nursing_now_campaign/en/
Publisher Copyright:
© 2021 The Authors. Journal of Nursing Scholarship published by Wiley Periodicals LLC on behalf of Sigma Theta Tau International.
Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.
PY - 2021/7
Y1 - 2021/7
N2 - PurposeTo identify which patient and hospital characteristics are related to nurse staffing levels in acute care hospital settings.DesignA cross-sectional design was used for this study.MethodsThe sample comprised 1,004 patients across 10 hospitals in the Andalucian Health Care System (southern Spain) in 2015. The sampling was carried out in a stratified, consecutive manner on the basis of (a) hospital size by geographical location, (b) type of hospital unit, and (c) patients’ sex and age group. Random criteria were used to select patients based on their user identification in the electronic health record system. The variables were grouped into two categories, patient and hospital characteristics. Multilevel linear regression models (MLMs) with random intercepts were used. Two models were fitted: the first was the null model, which contained no explanatory variables except the intercepts (fixed and random), and the second (explanatory) model included selected independent variables. Independent variables were allowed to enter the explanatory model if their univariate association with the nurse staffing level in the MLM was significant at p < .05.ResultsTwo hierarchical levels were established to control variance (patients and hospital). The model variables explained 63.4% of the variance at level 1 (patients) and 71.8% at level 2 (hospital). Statistically significant factors were the type of hospital unit (p = .002), shift (p < .001), and season (p < .001). None of the variables associated with patient characteristics obtained statistical significance in the model.ConclusionsNurse staffing levels were associated with hospital characteristics rather than patient characteristics.Clinical RelevanceThis study provides evidence about factors that impact on nurse staffing levels in the settings studied. Further studies should determine the influence of patient characteristics in determining optimal nurse staffing levels.
AB - PurposeTo identify which patient and hospital characteristics are related to nurse staffing levels in acute care hospital settings.DesignA cross-sectional design was used for this study.MethodsThe sample comprised 1,004 patients across 10 hospitals in the Andalucian Health Care System (southern Spain) in 2015. The sampling was carried out in a stratified, consecutive manner on the basis of (a) hospital size by geographical location, (b) type of hospital unit, and (c) patients’ sex and age group. Random criteria were used to select patients based on their user identification in the electronic health record system. The variables were grouped into two categories, patient and hospital characteristics. Multilevel linear regression models (MLMs) with random intercepts were used. Two models were fitted: the first was the null model, which contained no explanatory variables except the intercepts (fixed and random), and the second (explanatory) model included selected independent variables. Independent variables were allowed to enter the explanatory model if their univariate association with the nurse staffing level in the MLM was significant at p < .05.ResultsTwo hierarchical levels were established to control variance (patients and hospital). The model variables explained 63.4% of the variance at level 1 (patients) and 71.8% at level 2 (hospital). Statistically significant factors were the type of hospital unit (p = .002), shift (p < .001), and season (p < .001). None of the variables associated with patient characteristics obtained statistical significance in the model.ConclusionsNurse staffing levels were associated with hospital characteristics rather than patient characteristics.Clinical RelevanceThis study provides evidence about factors that impact on nurse staffing levels in the settings studied. Further studies should determine the influence of patient characteristics in determining optimal nurse staffing levels.
KW - hospital; personnel management
KW - Hospitals
KW - inpatients
KW - multilevel analysis
KW - nursing staff
UR - http://www.scopus.com/inward/record.url?scp=85104559399&partnerID=8YFLogxK
U2 - 10.1111/jnu.12649
DO - 10.1111/jnu.12649
M3 - Article
AN - SCOPUS:85104559399
SN - 1527-6546
VL - 53
SP - 468
EP - 478
JO - JOURNAL OF NURSING SCHOLARSHIP
JF - JOURNAL OF NURSING SCHOLARSHIP
IS - 4
ER -