Predictors of physical restraint use on critical care units: An observational structural equation modeling approach. 2021

Danielle Olds, and Emily Cramer
Research Assistant Professor, University of Kansas School of Nursing USA. Electronic address: danielle.m.olds@gmail.com.

BACKGROUND Modifiable unit characteristics, including nurse work environment, education, certification, and staffing have been shown to impact patient safety. Physical restraints are an important patient safety issue, however the relationships between these modifiable unit characteristics and physical restraint use on critical care units has not been explored. OBJECTIVE Our objective was to determine the role of nursing work environment, nurse education and certification, and nurse staffing on physical restraint use. METHODS The study was a secondary analysis of 2017-2018 unit-level restraint rates from the National Database of Nursing Quality Indicators linked to Registered Nurse survey and hospital characteristics data. METHODS Work environment was examined at the unit-level using total Practice Environment Scale of the Nursing Work Index and subscale scores. Unit-level nurse expertise included the percent of nurses with at least a Bachelor of Science in Nursing degree and percent with certification. Nurse staffing was the unit-level Registered Nurse hours per patient day and skill mix. Analyses included descriptive statistics, bivariate correlations, and Structural Equation Modeling. We used a first order model to estimate predicted restraint use from the five subscales of the Practice Environment Scale of the Nursing Work Index. A higher order model predicted restraint use from a total work environment factor score. RESULTS The sample included 408 critical care units from 226 hospitals. Mean restraint rate was 15% (SD 12, range 0-53%). In the first-order model, the Collegial Nurse-Physician Relations subscale had a positive relationship with restraint use (β = 0.167, 95% confidence interval 0.010-0.333). In the higher order model, the total work environment score had a negative relationship with restraint use (β = -0.088, 95% confidence -0.178- -0.014). Registered Nurse hours per patient day had a negative relationship to restraint use in both the first order (β = -0.114, 95% confidence interval -0.222--0.025) and higher order models (β = -0.117, 95% confidence interval -0.223- -0.012). CONCLUSIONS We found that better nurse-physician relationships were associated with higher restraint use. This finding is supported by previous literature and may reflect physician trust in nursing judgement when using restraints. However, a better work environment overall was associated with lower restraint rates. Further, Registered Nurse hours per patient day, but not skill mix, was associated with lower restraint rates. We conclude that improving the overall nurse work environment and nurse staffing, as well as using interprofessional interventions, may be successful in decreasing restraint use on critical care units.

UI MeSH Term Description Entries
D009741 Nursing Staff, Hospital Personnel who provide nursing service to patients in a hospital. Hospital Nursing Staff,Hospital Nursing Staffs,Nursing Staffs, Hospital,Staff, Hospital Nursing,Staffs, Hospital Nursing
D010561 Personnel Staffing and Scheduling The selection, appointing, and scheduling of personnel. Staffing and Scheduling,Personnel Staffing,Work Schedule,Schedule, Work,Scheduling and Staffing,Staffing, Personnel,Work Schedules
D012149 Restraint, Physical Use of a device for the purpose of controlling movement of all or part of the body. Splinting and casting are FRACTURE FIXATION. Immobilization, Physical,Physical Restraint,Physical Immobilization,Physical Restraints,Restraints, Physical
D003422 Critical Care Health care provided to a critically ill patient during a medical emergency or crisis. Intensive Care,Intensive Care, Surgical,Surgical Intensive Care,Care, Critical,Care, Intensive,Care, Surgical Intensive
D003430 Cross-Sectional Studies Studies in which the presence or absence of disease or other health-related variables are determined in each member of the study population or in a representative sample at one particular time. This contrasts with LONGITUDINAL STUDIES which are followed over a period of time. Disease Frequency Surveys,Prevalence Studies,Analysis, Cross-Sectional,Cross Sectional Analysis,Cross-Sectional Survey,Surveys, Disease Frequency,Analyses, Cross Sectional,Analyses, Cross-Sectional,Analysis, Cross Sectional,Cross Sectional Analyses,Cross Sectional Studies,Cross Sectional Survey,Cross-Sectional Analyses,Cross-Sectional Analysis,Cross-Sectional Study,Cross-Sectional Surveys,Disease Frequency Survey,Prevalence Study,Studies, Cross-Sectional,Studies, Prevalence,Study, Cross-Sectional,Study, Prevalence,Survey, Cross-Sectional,Survey, Disease Frequency,Surveys, Cross-Sectional
D006801 Humans Members of the species Homo sapiens. Homo sapiens,Man (Taxonomy),Human,Man, Modern,Modern Man
D000077272 Latent Class Analysis A statistical algorithm used to analyze clusters of observed variables by constructing categorical unobserved or latent segment based on weighted analysis and the average probabilities. Such latent classes are used to infer variables whose relationships are not directly observed. In biomedical research, it is often used to categorize data that allows the determination of symptom clusters. Latent Class Model,Latent Variable Modeling,Probabilistic Latent Semantic Analysis,Structural Equation Modeling,Latent Class Analyses,Latent Class Models,Latent Variable Modelings,Model, Latent Class,Modeling, Latent Variable,Modeling, Structural Equation,Structural Equation Modelings,Variable Modeling, Latent

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