New 12-Item Instrument Measures How Nurses Ration Care Under Resource Pressure

Nurses in Iranian hospitals now have a validated tool to measure how often they are forced to ration patient care due to overwhelming resource constraints. A research team affiliated with Kermanshah University of Medical Sciences has designed and psychometrically evaluated a 12-item Nursing Care Rationing Instrument that demonstrates strong internal consistency, temporal stability, and a robust unidimensional structure across multiple analytical frameworks. The instrument, developed through a sequential multi-phase validation process involving more than 300 clinical nurses across five tertiary teaching hospitals, addresses a critical gap in the quantitative assessment of care rationing in contexts where existing tools developed in Switzerland, Indonesia, and Taiwan cannot be directly applied due to fundamentally different cultural, social, and economic healthcare environments.

Nursing care rationing refers to the allocation of limited nursing resources in a manner that results in some patients receiving less care than they require. It occurs when shortages of nurses, equipment, or time compel clinical staff to make difficult decisions about which patients receive which interventions. The phenomenon is conceptually distinct from workload, which describes the volume of demands placed on a nurse, and from missed care, which describes the end result of omitted tasks. Rationing focuses on the active cognitive decision-making and prioritization process that leads to those omissions. It is recognized as a global problem within healthcare systems, posing significant challenges to patient safety and quality of care. Adverse outcomes associated with care rationing include increased mortality rates, falls during hospitalization, pressure ulcers, nosocomial infections, reduced patient satisfaction, and emotional exhaustion among nurses.

The research team employed a combined inductive and deductive methodology for item generation. The inductive phase consisted of individual qualitative interviews with 15 clinical nurses exploring their lived experiences of care prioritization, time scarcity, and resource allocation. These interviews were transcribed and analyzed using conventional content analysis to establish a conceptual foundation. Based on these findings, nursing care rationing was conceptualized as a complex process of clinical decision-making necessitated by severe systemic constraints and high patient volumes, involving the cognitive prioritization and equitable allocation of limited care resources among multiple patients. The deductive phase involved a comprehensive review of relevant literature to capture existing theoretical dimensions. An initial pool of 78 items was generated, which was subsequently refined to 45 items after merging overlapping entries and removing irrelevant ones.

The validation process proceeded through six distinct phases, each recruiting independent cohorts of participants to avoid sample contamination. Face validity was assessed by 10 registered clinical nurses using both qualitative and quantitative methods. In the quantitative approach, the Item Impact method required each nurse to rate items on a five-point Likert scale, with an impact score of 1.5 or higher indicating appropriateness. Eleven items fell below this threshold and were revised based on qualitative feedback regarding difficulty, irrelevance, and ambiguity. Content validity was then evaluated by a panel of 10 university faculty members specializing in nursing management and instrument development. The Content Validity Ratio eliminated 23 items scoring below the 0.62 threshold at a 95 percent confidence level, while the modified kappa-based Content Validity Index confirmed the remaining 22 items without further eliminations.

Initial reliability testing with an independent sample of 30 clinical nurses produced a Cronbach’s alpha of 0.871. Item-total correlation analysis revealed that three items had correlations below 0.1 and were removed, increasing the alpha coefficient to 0.942. The resulting 19-item instrument then entered the construct validity phase, where 220 clinical nurses from diverse hospital departments completed the scale. The sample included nurses from general internal medicine and medical subspecialties (30.9 percent), surgical departments (24.1 percent), emergency units (16.4 percent), pediatric and obstetric units (12.7 percent), intensive care units (10.0 percent), and specialized outpatient units (5.9 percent).

Exploratory factor analysis using Principal Axis Factoring extraction was conducted on the 19-item pool. The Kaiser-Meyer-Olkin measure of sampling adequacy was 0.947, indicating excellent suitability for factor analysis, and Bartlett’s test of sphericity was significant at p less than 0.001. Parallel analysis confirmed that only the first factor had an eigenvalue exceeding the 95th percentile of eigenvalues derived from simulated random data, with an observed eigenvalue of 8.907 compared to a simulated mean of 1.386. Seven items were systematically eliminated during iterative analytical steps due to low extraction communalities, factor loadings below the 0.30 threshold, or significant cross-loadings. The final 12-item unidimensional model explained 71.97 percent of the total variance, with factor loadings ranging from 0.743 to 0.918 and communalities ranging from 0.552 to 0.842.

To corroborate the unidimensional structure from a network psychometrics perspective, the researchers applied Exploratory Graph Analysis, which conceptualizes psychological constructs as networks of interacting items. Prior to structure estimation, a Unique Variable Analysis identified moderate to large topological overlaps among specific item pairs, such as a weighted topological overlap of 0.377 between two items related to time constraints. These items were retained given their theoretical relevance to the core construct. The EGA algorithm supported a single-community network structure for the 12 items, consistent with the factor analysis solution. Dimension stability evaluated through 500 bootstrap resampling iterations indicated a median dimension of 1 with a 95 percent confidence interval of 1 to 1 and a standard error of 0. Item stability analysis demonstrated consistent replication rates of 100 percent across all 12 nodes, confirming that items consistently clustered within the single identified community throughout all bootstrap iterations.

Non-parametric Item Response Theory was applied through Mokken Scale Analysis to investigate the hierarchical properties of the instrument. The overall Loevinger’s scalability coefficient was 0.762, classified as a strong unidimensional scale. Item-level scalability coefficients ranged from 0.677 to 0.817, with all 12 items substantially exceeding the minimum retention threshold of 0.30. This strong scalability implies a cumulative property within the instrument, meaning that nurses who endorse items representing more severe forms of care rationing are consistently likely to also endorse items representing lower-level rationing behaviors. A Monte Carlo simulation generating 1,000 random datasets based on the empirical correlation matrix of the final 12 items produced a mean Tucker’s Congruence Coefficient of 1.00, representing a perfect structural match with the empirical model. The statistical power to recover the factor structure was 100 percent, indicating that every simulated sample successfully achieved a congruence coefficient of at least 0.90.

Final reliability assessment was conducted with a separate cohort of 30 clinical nurses. The overall Cronbach’s alpha for the 12-item instrument was 0.953, and McDonald’s omega was 0.968. The average inter-item correlation was 0.633, which exceeds the theoretically desirable upper limit of 0.50 and suggests a degree of item redundancy. The researchers noted that this semantic overlap is expected in clinical practice because rationing behaviors are highly interconnected; a nurse compelled to ration one aspect of care due to time constraints is likely rationing other closely related tasks simultaneously. Temporal stability was evaluated through a test-retest method with a two-week interval using a two-stage alphanumeric coding procedure that preserved participant anonymity. The intraclass correlation coefficient for the entire instrument between the two measurements was 0.969, with a 95 percent confidence interval ranging from 0.953 to 0.985, well above the 0.75 threshold considered indicative of acceptable reliability.

The final instrument consists of 12 items rated on a five-point Likert scale ranging from 1 (never) to 5 (always), with total scores ranging from 12 to 60. Higher scores reflect a greater frequency and extent of nursing care rationing. The items address factors including the number of patients under care, time constraints, equitable distribution of resources among patients, omission of patient and family education, delegation of care tasks to patients and their companions, and prioritization of therapeutic care. The researchers acknowledge several limitations: the study was conducted within a single geographic region, reliance on self-reported data may introduce social desirability bias, and a formal Confirmatory Factor Analysis on an independent sample remains a recommended next step. Future research should evaluate criterion-related, convergent, and discriminant validity across diverse healthcare settings and cultural populations. The instrument provides nursing managers with a context-specific metric for identifying units facing severe resource constraints, facilitating data-driven decisions for equitable staff allocation and targeted interventions to reduce workload-induced burnout.

Subject of Research: Development and psychometric validation of a Nursing Care Rationing Instrument for Iranian clinical settings

Article Title: Design and Psychometric Evaluation of a Nursing Care Rationing Instrument

Article References: Mohammadi, M. M., Mohammadi, M., & Bakhtiyari, S. (2026). Design and Psychometric Evaluation of a Nursing Care Rationing Instrument. Nursing Open, 13(10), Article e70933. https://doi.org/10.1002/nop2.70933

Image Credits: AI Generated

DOI: 10.1002/nop2.70933

Keywords: nursing care rationing, instrument development, psychometric validation, exploratory factor analysis, network psychometrics, Mokken scale analysis, patient safety, nursing workforce, Iran healthcare, missed care, resource allocation, reliability testing