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A descriptive analysis of implicit rationing of nursing care: frequency and patterns in Texas.

HEALTH CARE RATIONING is a politically charged issue that evokes deep emotional reactions from all sectors within the industry: politicians, patients, payers, and providers. Despite widespread agreement that resources to sustain current trends in health care spending are inadequate, the role of rationing in U.S. health care reform is polarizing. This polarization was exemplified by the partisan opposition to the presidential nominee for administrator of the Centers for Medicare & Medicaid Services (CMS) and implementation of the Affordable Care Act (ACA) (Goodman, 2011). The confirmation of Dr. Don Berwick as CMS administrator was blocked based on a single explicitly identified issue: his previous comments regarding the inevitability of health care rationing in the United States (Pear, 2011). Likewise, much of the opposition to the ACA has been attributed to the characterization of the proposed Independent Medicare Advisory Committee (IMAC) as a rationing committee.

The ACA charges the IMAC to issue annual reports with recommendations regarding provider reimbursement for eligible services (Coleman, 2011). The ACA language stipulates the following in regard to IMAC recommendations: (a) they are intended to reduce spending, (b) they cannot increase spending, (c) they must protect access to necessary and evidence-based care, and (d) they cannot include rationing of care. However, the role of the IMAC in defining necessary and evidence-based care continues to be a linchpin in the interpretation of IMAC as a form of economic rationing. For example, it was suggested in situations where advanced life support services are determined to provide minimal potential benefit, the ACA could deny payment and in effect ration these services. The depiction of the IMAC as a "death panel" with the authority to pull the plug on grandma ignited fear among many Medicare beneficiaries and continues to be a source of much political rhetoric.

Rationing is a term used to describe allocation of resources in the context of scarcity (Brock, 2007; Brody, 2012), which exists in health care when the need for services exceeds the resources to provide services. Thus, health care rationing involves decisions to withhold beneficial services for reasons associated with inadequate resources. Rationing is therefore best discussed in the context of health care decision making (Mechanic, 1997). Decisions about health care are made at multiple levels within the system: (a) the macro level where policy is established by governments, health authorities, insurance plans, etc.; (b) the meso level where organizational budgets are established by organizational administrators; and (c) the micro level where care is delivered by clinician providers (Russel, 2002). Macro and meso level decisions are most commonly made by administrative authorities in the form of explicit policies and fixed budgetary allowances. Such policies are typically rule-based and applied broadly (e.g., eligibility criteria for beneficiaries and definitions of benefits and reimbursable services). Decisions of this form that result in withholding of health care are administrative and/or political in nature and are considered a form of explicit rationing (Goodman, 2011; Mechanic, 1997). In contrast, decisions about withholding care at the micro level are typically applied to specific patients and contexts based on the judgment of front-line clinicians. Decisions of this form are clinical and discretionary in nature and are considered a form of implicit rationing, also known as bedside rationing (Mechanic, 1997; Ubel & Goold, 1997).

Implicit Rationing

Most of the political rhetoric and media attention about health care rationing has centered on explicit rationing while implicit rationing has received comparatively little attention. This is not surprising because policymaking is more visible to the public than bedside clinical decision making. Consequently, implicit rationing has been characterized as a form of hidden rationing that remains largely invisible to the public, and possibly to the patients themselves (Lauridsen, Norup, & Rossel, 2007). The absence of explicit rationing policies does not, therefore, ensure an absence of rationing per se. It has been argued that in the absence of explicit rationing policies, clinicians faced with fixed budgetary allowances and variable care demands are forced to engage in more implicit rationing (Brody, 2012; Goodman, 2011). In essence, cost-containment strategies shift the burden of responsibility for rationing decisions from policymakers to clinicians. Additionally, the decision-making process is shifted underground from the visible world of policymaking to the invisible world of clinical decision making. Invisible decisions are inherently more difficult to measure and evaluate, and invisible decision makers more difficult to hold accountable.

As prescribes of medical care, physicians are often viewed as gatekeepers for access to health care. Not surprisingly, the literature regarding implicit rationing has primarily emerged from the discipline of medicine (Ayres, 1996; Hurst et al., 2006; Perneger, Martin, & Bovier, 2002; Strech, Persad, Marchmann, & Danis, 2009; Strech, Synofzik, & Marckmann, 2008). Three conclusions relative to implicit rationing among physicians are supported by qualitative and quantitative evidence: (a) it is perceived to be an inevitable part of routine practice; (b) it is influenced by multiple determinants (e.g., bed availability, physician attitudes, and patient ability to exercise pressure); and (c) it is associated with negative provider outcomes (e.g., perceived role conflict and ethical distress). However, health care extends beyond medicine to include multiple clinical disciplines and access to medical care requires more than a physician prescription. Although medical care may be prescribed by physicians, it is largely accomplished through the interdependent and collaborative efforts of other disciplines, particularly nursing. The gatekeeping function for access to health care is more accurately depicted as being shared among the clinical disciplines. A complete picture of implicit rationing therefore requires the clinical decisions of other disciplines also be examined.

Implicit Rationing of Nursing Care Defined

The phenomenon of implicit rationing among nurses has only recently appeared in the literature. A team of Swiss investigators was the first to define, describe, and measure implicit rationing of nursing care and continue to pioneer the field (Schubert, Glass, Clarke, Schaffert-Witvliet, & De Geest, 2007). Implicit rationing of nursing was defined by this team as "the withholding of or failure to carry out necessary nursing measures for patients due to the lack of nursing resources (staffing, skill mix, time)" (p. 417). The phenomenon of implicit rationing of nursing care was subsequently evaluated among hospital nurses from three countries (Switzerland, Canada, and Greece) using some variation of the self-report instrument, Basal Implicit Rationing of Nursing Care (BERNCA) (Ausserhofer et al., 2013; Papastavrou, Andreou, Tsangari, Schubert, & De Geest, 2014; Rochefort & Clarke, 2010; Schubert et al., 2007; Schubert et al., 2008; Schubert, Clarke, Glass, Schaffert-Witvliet, & De Geest, 2009; Schubert, Clarke, Aiken, & De Geest, 2012; Schubert et al., 2013). The BERNCA prompts nurses to rate the frequency with which they were unable to complete a list of com mon nursing care activities, when needed, during the previous seven shifts using a Likert-type scale.

The findings from this emerging body of literature suggest the following in regard to implicit rationing of nursing care: (a) it is routinely practiced among hospital nurses, (b) it occurs across all categories of nursing care (e.g., physical care, coordination of care, documentation of care, and emotional care), (c) it is associated with multiple negative patient outcomes (mortality, patient falls, decubitus ulcers, nosocomial infections, and patient satisfaction) even at low thresholds, (d) it is a stronger predictor of patient outcomes than nurse staffing indices, and (e) perceived adequacy of staffing resources is the strongest predictor of implicit rationing. Moreover, patterns of implicit rationing suggest nurses ration care based on an informal system of prioritization that favors elements of care with a direct and immediate effect on patient outcomes and elements of care that require predictable time consumption (Schubert et al., 2013).

The frequency and patterns of implicit rationing of nursing care in the United States are not known. Furthermore, the practice of implicit rationing may not be generalizable across countries due to the variability in systems of health care delivery, health care reimbursement, and nursing education. Given the U.S. aversion to explicit rationing and the prevalence of cost-containment initiatives among U.S. health care organizations, it is imperative implicit rationing practices be more thoroughly explored and evaluated. Therefore, the purpose of this study was to examine the phenomenon of implicit rationing among nurses in Texas.

Study Design and Sample

A cross-sectional survey design was used to examine the frequency and pattern of implicit rationing in a stratified random sample of medical-surgical nurses. Nurses practicing on or managing inpatient medical-surgical nursing units were identified from the nurse licensure database maintained by the Texas Board of Nursing. Registered nurses (RNs) and licensed vocational nurses (LVNs) meeting the following criteria were included: complete Texas address; primary practice setting listed as inpatient hospital care; clinical practice area listed as general practice, geriatrics, medical-surgical, oncology, or rehabilitation; and position type listed as staff nurse/general duty, head nurse, or assistant head nurse. Eligible nurses were then stratified into three practice groups: direct-care LVNs, direct-care RNs, and nurse managers (NMs). The three practice groups were subsequently stratified by the 11 Texas Health and Human Services Regions (Texas Department of State Health Services, 2015) based county of employment (see Figure 1).

A random sample of 3,529 nurses was generated: 1,200 direct-care RNs (109-110 from each region); 1,200 direct-care LVNs (109-110 from each region); and 1,129 NMs. When sampling from the NM group, some of the regions were collapsed due to low cell sizes. Regions were combined based on geographic proximity and sampled as follows: regions 1 and 2 (150); region 3 (150); regions 4 and 5 (150); region 6 (150); region 7 (150); region 8 (150); regions 9 and 10 (126); and region 11 (103). A total of 29 addresses could not be confirmed as legitimate and these nurses were subsequently excluded to yield a final sample of 3,500.

Instrumentation and Data Collection

The primary variable of interest was implicit rationing of nursing care as defined by Schubert and associates (2007) and was measured using a self-report survey instrument adapted from the English version of the BERNCA. Adaptation of the BERNCA has been previously described (Jones, 2014) and involved revisions to the list of nursing care activities queried: (a) to ensure inclusion of activities emphasized by U.S. regulatory agencies, and (b) to ensure use of terminology recognizable to U.S. nurses. The newly adapted instrument, the Perceived Implicit Rationing of Nursing Care (PIRNCA), is a 31-item inventory of nursing care activities within the domain of U.S. nursing practice and common to the U.S. inpatient hospital environment. No changes were made to the prompt or response options: respondents are prompted to rate the frequency in which they (or their staff) were unable to complete each activity when needed during the previous seven shifts due to inadequate resources; and frequency ratings include "not needed," "never," "rarely," "sometimes," and "often." The proposed PIRNCA was evaluated by a panel of experts (practicing nurses) for relevance, comprehension, and comprehensiveness prior to the study. Minor changes were made based on panel feedback to enhance item clarity.

Survey instruments to assess demographic variables and constructs related to implicit rationing (for assessment of concurrent validity) were also used: demographic variables (age, gender, education, experience, and work setting) were assessed with a 15-item questionnaire; attributes of the work environment were assessed using the 85-item Essentials of Magnetism II (EOMII) instrument; quality of care (QOC) was assessed using a 10-point single-item indicator; and overall job satisfaction was assessed with a 10-point single-item indicator (Kramer et al., 2007; Kramer & Schmalenbeig, 2004, 2005; Schmalenberg & Kramer, 2008). Acceptable psychometric properties of the measures for work environment, QOC, and overall job satisfaction (OJS) have been reported previously.

A modified Dillman Total Design methodology guided data collection procedures (Dillman, 2007). After review and approval by the institutional review board at the investigator's institution, survey packets (study description, letter of invitation, survey instruments, and postage-paid return envelope) were mailed to nurses in the random sample. Two additional reminder letters were mailed to non-responders at 3-4 week intervals. No incentives were offered or provided. Return of completed surveys was accepted as consent to participate.

Analysis and Results

SPSS version 19 was used for all analyses. Data were examined for the frequency and patterns of missingness; no indications for imputation of data or exclusion of survey items were identified. Group equivalence was assessed by one-way Analysis of Variance (ANOVA). The internal structure of the PIRNCA was evaluated using principle components analysis (PCA) and reliability was assessed using Cronbach's alpha. Concurrent validity was assessed using bivariate correlations (Pearson's) between mean PIRNCA scores and measures of related concepts: attributes of the work environment (EOMII total and subscale scores), QOC, and OJS. Sample weights were computed as the proportion of the nurse population in each of the 11 public health regions to the proportion of the sample from each region and applied to subsequent analyses. Descriptive statistics (frequency counts, percentages, and means) were used to assess rationing patterns.

An overall response rate of 7% (242 surveys returned of 3,500 surveys mailed) was achieved. Fifteen cases were excluded: 1 for incompleteness and 14 due to listed practice settings inconsistent with eligibility criteria. A total of 226 respondents were included in the final sample for analysis: 94 RNs, 63 LVNs, and 69 NMs. Sample characteristics are provided in Table 1. The respondents were primarily middle-aged (mean age 49 years) White (67%) women (89%) working full time (89%) 12-hour shifts (60%) in a metropolitan service area (MSA). The majority of the RN respondents were educated at or above the baccalaureate level (53%). Most of the respondents had access to unlicensed assistive personnel (81%) and half worked in settings where the charge nurse did not have a patient assignment. Less than half of the respondents (44%) worked in a Magnet[R] or Pathway to Excellence[R] hospital (American Nurses Credentialing Center, 2014). Representation from each of the 11 public health regions was present in the sample; each region accounted for 4%-14% of the total sample.

Detailed results of the analyses of internal structure and concurrent validity for the newly adapted PIRNCA were reported previously (Jones, 2014). Similar to the parent instrument (BERNCA), PCA supported a single-factor solution with stable-factor loadings for the PIRNCA. Significant variability was explained (55%) and internal consistency was excellent (a=0.97). Moderate inverse relationships between mean PIRNCA scores and measures of related concepts were demonstrated: EOM II (r = -0.44), QOC (r = -0.56), and OJS (r = -0.48). The weighted mean PIRNCA score was 1.15 (SD = 0.66) with individual mean scores ranging from 0 to 2.90. Specifically, 42% of the nurses said they rationed care never to rarely (0-1), 45% responded rarely to sometimes (1.03-2), and 13% responded sometimes to often (2.03-2.90). Mean scores were equivalent across practice groups (RNs = 1.53, LVNs =1 .42, and NMs=1.59) [F.sub.(2,223)] = 0.54, p = 0.58. Because the groups were not significantly different, all practice groups were combined in subsequent analyses.

Some degree of rationing on at least one of the nursing care activities was reported by almost all of the respondents (98%) and most (97%) rationed multiple activities. Item-level rationing frequencies ranged from 77 (34%) to 208 (93%) and are presented in Table 2. With the exception of three activities, each care activity was rationed by over half of the nurse respondents. The mean number of activities rationed per respondent was 21 (SD = 9.5) with quartile frequency ranges as follows: 0-15, 16-24,' 25-29, and 30-31. Individual item mean scores ranged from 0.44 (less than rarely) to 1.72 (more than rarely). The care activities most frequently rationed (top 25%) included providing timely response to patient need or request (1.72), review documentation by the care team (1.55), provide routine hygiene (1.52), document all nursing interventions/ care (1.50), provide patient teaching (1.48), provide emotional or psychological support (1.45), have an important conversation with other team members (1.41), and evaluate the plan of care (1.36). The care activities least frequently rationed (bottom 25%) included administering enteral nutrition (0.44), adhering to infection control guidelines (0.57), administering medications (0.62), providing wound care (0.80), changing dressings (0.84), monitoring physiological status (0.96), changing soiled linen (1.03), and changing intravenous sites or tubing (1.03).

Weighted mean PIRNCA scores ranged from 0.83 to 1.42 across the public health regions (see Figure 2). There was a trend toward higher PIRNCA scores in the upper and lower south Texas regions (regions 8 and 11); however, no statistically significant differences were demonstrated ([F.sub.10,215] = 1.43; p=0.17). Likewise, PIRNCA scores for MSAs (1.53) and non-MSA's (1.50) were not significantly different ([F.sub.1,224] = 0.04; p = 0.85).

Discussion

Research findings should always be interpreted in the context of identified methodological limitations. Two aspects of the study sample potentially limit the precision and generalizability of findings in this study: the low response rate and the inclusion of medical-surgical nurses from a single state. Despite the incorporation of several response-enhancement strategies into data collection procedures, the response rate of 7% was below expected based on previous survey studies involving nurses. For example, Asch, Jedrziewski, and Christakis (1997) reported a mean response rate of 60% for survey studies published in the medical literature and recent survey studies involving nurse work environment issues have reported response rates ranging from 35%-52% (Aiken, Clarke, Sloan, Sochalski, & Silber, 2002; Aiken et ah, 2010). Consequently, the potential for nonresponse bias must be acknowledged (MacDonald, Newborn-Cook, Schopflocher, & Richter, 2009). Regional variation in practice patterns have been documented in other disciplines (Fisher et al., 2003a, 2003b). Consequently, the potential for variability in implicit rationing practices across U.S. practice settings must also be acknowledged.

Research findings should also be interpreted in the context of other studies. Despite the sample-related limitations, the frequency and patterns of implicit rationing of nursing care reported in this study are remarkably similar to what has been reported in other heterogeneous samples with higher response rates (60%-72%). Comparable to the mean rationing frequency of 1.15 (rarely) in the current study, mean rationing scores among nurses in Switzerland and Greece have consistently been in the "less than rarely" range (Papastavrou et al., 2014; Schubert et al., 2007; Schubert et al., 2009; Schubert et al., 2013). Also consistent with the current study, 96%98% of nurses in the European studies rationed at least one of the queried activities during the previous seven shifts (Schubert et al., 2008; Schubert et al., 2013) and most rationed over half of the activities queried (Papastavrou et al., 2014).

The low reported frequency of implicit rationing based on mean scores of the BERNCA and/or PIRNCA can be deceptively reassuring and should be interpreted in the context of the hospitalized patient experience. Because patients receive care from multiple nurses throughout an inpatient stay, the cumulative frequency of implicit rationing across nurses assigned to each patient must be considered. Consistent findings from multiple studies indicate hospitalized patients are likely to receive care from multiple nurses who are rationing multiple care activities (Papastavrou et al., 2014; Schubert et al., 2007; Schubert et al., 2008; Schubert et al., 2009; Schubert et al., 2013). The cumulative frequency of rationing experienced by patients is therefore likely to be more than rare. Previous reports of significant inverse relationships between low levels of implicit rationing and adverse patient outcomes is therefore not surprising (Schubert et al., 2008; Schubert et al., 2012). For example, Schubert and colleagues (2008) demonstrated a 0.5-unit increase in mean rationing scores was associated with as much as a threefold increase in adverse patient outcomes.

In contrast, a significant relationship between quality of care and discretionary utilization of physician-related services (arguably a form of implicit rationing) was not supported in the National Study of Medicare Beneficiaries (Fisher et al., 2003b). However, significant regional variation in discretionary utilization of physician-related services was identified and partially attributed to the physician workforce distribution (Fisher et al., 2003a). Beneficiaries in regions with the highest physician workforce capacity received 60% more physician services than those in geographical regions with lower physician workforce capacities. Regional variation in nursing workforce capacity across Texas has been identified (Texas Department of State Health Services, 2013); counties in the upper and lower South Texas regions (regions 8 and 11) were noted to have comparatively low workforce capacities. However, the trend toward higher implicit rationing of nursing care in these regions did not reach statistical significance in the current study. Based on a post hoc power analysis, it was determined this study lacked sufficient power to detect regional differences.

In the European studies, care activities related to emotional and psychological support were rationed most frequently while prescribed physical/physiological treatment-related activities were rationed least frequently (Papastavrou et al., 2014; Schubert et al., 2013). Similar rationing preferences were identified in the current study (see Table 2). The following interpretations regarding rationing preferences were offered by Schubert and co-authors (2013): (a) nurses are less likely to ration activities that have potential for a direct and immediate effect on a patient's health condition; (b) nurses are more likely to ration activities that cannot be abbreviated (no opportunity for short cuts to decrease the time requirement); and (c) muses are more likely to ration activities with a time requirement that is unknown and/or difficult to estimate. It is noteworthy the elements of care least likely to be rationed based on these suggested preferences tend to be task-related activities associated with a high degree of standardization (e.g., changing wound dressings and administering medications). In contrast, elements of care that require higher levels of cognitive function (e.g., evaluation of care) and/or higher levels of individualization (e.g., teaching and emotional support) are more likely to be rationed.

Interpretation of preferences and patterns of implicit rationing among hospital nurses is complex. Mean scores on measures of implicit rationing only reveal the frequency with which rationing decisions were made. Preference patterns reveal little about the decision-making process and yield an incomplete picture of the quality and outcome of those decisions. Decision-making process models typically involve analysis of cost and benefit relative to available choice options (Lahno, 2007; Vanberg, 2002). Rationality is assumed and decision quality is evaluated based on the principle of utility maximization. Each care activity represents a choice option that carries some health benefit for patients and/or their families and sometime cost for the nurse provider. The choice option to withhold a care activity also is associated with costs and benefits; the benefit being to free up time to complete another care activity, and the cost being the risk for a negative patient health outcome. Each rationing decision involves withholding one benefit in order to apply another, while concomitantly increasing risk for one negative outcome in order to decrease risk for another. The decision (choice) is said to reflect preferences for expected outcomes and beliefs about the relationship between specific actions and outcomes (Vanberg, 2002).

Within the framework of utility maximization, the best decision is one that results in the most good. Completion of care activities associated with greater benefit in lieu of activities associated with lesser benefit are therefore perceived as good decisions. There is benefit to addressing the immediate safety and physiologic health care needs of hospitalized patients, and immediate risk in not addressing them. Likewise, there is benefit to anticipating and planning for future health care needs (e.g., discharge planning) and providing emotional support to facilitate coping and adaptation. The consequences of withholding these benefits may not be immediate but they can be significant for the patient and the health care system. If the identified preference patterns (addressing immediate and direct physiologic needs through completion of standardized tasks) represent a greater good, then it can be argued bedside nurses are making good rationing decisions. If these preference patterns do not represent the greater good, then the contrary can be concluded --bedside nurses are making poor rationing decisions. In either case further action is warranted by nurse leaders: (a) strategies should be developed to mitigate the risk of rationed care, and (b) strategies should be developed to support rationing preference patterns that reflect the greater good.

It is important to note rational decision-making models assume ideally rational actors and un bounded rationality. Characteristics of ideally rational decision makers include: (a) they engage in intentional deliberation of choice options, (b) they have knowledge of all consequences associated with each choice option, (c) they can assign probabilities to each consequence, and (d) they have unlimited capabilities for information processing (Lahno, 2007; Vanberg, 2002). It has been argued, however, that ideally rational actors do not exist and that rationality is bounded by limitations in the human capacity for perception, memory, interpretation, and calculation (Monroe & Maher, 1995). These limitations are especially pertinent to the phenomenon of implicit rationing of nursing care. While it is clear bedside nurses are making decisions relative to implicit rationing, it is not clear whether these decisions are the result of conscious and intentional deliberation over choice options.

The bedside nurse's capacity for sustained situational awareness and rapid information processing is often challenged by the complexity of the current nurse work environment (Sitterding, Broome, Everett, & Ebright, 2012). Multiple studies using mixed methods indicate the cognitive work associated with a typical acute care patient assignment is highly complex and is associated with a cognitive pathway characterized by cognitive overload, cognitive stacking, and frequent interruptions (Ebright, Patterson, Chalko, & Render, 2003; Potter et al., 2005; Wolf et al., 2006). The complexity of the environment and cognitive work of bedside nurses is integrally related to the experience of time scarcity. Thus, the very conditions most likely to create the need for implicit rationing are the same conditions that hinder rational decision making. Furthermore, scientific knowledge to support assigning the probability of specific consequences to specific nursing action sets is often lacking. For example, neither of the following probabilities is known: (a) developing a postoperative wound infection associated with one missed dressing change, and (b) a hospital readmission associated with no teaching about the signs and symptoms of hyperglycemia. Collectively these conditions do not support a model of ideally rational decision making by bedside nurses relative to implicit rationing.

Conclusions and Implications

This study provides evidence that implicit rationing may be a routine component of clinical decision making among medical-surgical nurses in at least one U.S. state. Moreover, it suggests that, when faced with time scarcity, medical-surgical nurses may favor completion of activities to address direct and immediate physiological health needs over psychological and future health needs. The documented presence of implicit rationing of nursing care in the U.S. health care system has important implications for research, practice, and education. Research is needed to determine if the relationships documented in other countries between implicit rationing and adverse patient outcomes are also present in the United States. If these relationships are supported then implicit rationing may serve as an important quality indicator.

The frequency and patterns of rationed care can be used by nurse leaders to evaluate multiple levels of decision making that affect care delivery. For example, the primary condition for implicit rationing (time scarcity) is the byproduct of macro and meso-level decisions related to resource allocation. The volume/frequency of rationed care is an outcome of administrative decision making and reflects the need for rationing by clinicians. Changes in the need for rationing as measured by the volume/frequency of rationed care can therefore be used as a metric to evaluate administrative decisions. Decisions resulting in a decreased need for rationing should be advocated by nurse leaders. In contrast, patterns of rationed care are the byproduct of micro-level decisions of direct-care providers related to how care is rationed. Patterns of rationing reflect the choice preferences of clinicians and can be used to predict which elements of care are most likely to be left unfinished during periods of time scarcity. Armed with this information, nurse leaders can better anticipate problems and plan accordingly.

A better understanding of the decision-making process is needed to guide quality assessment of rationing preference patterns and to develop strategies to support "good" rationing decisions. Nurse leaders must create opportunities to learn more about the underlying rationale for rationing preferences and what information is considered when deciding among choice options. Finally, more research is needed to assess relationships between specific preference patterns (e.g., completion of direct and immediate physiologic care activities) and patient outcomes to support value judgments about the quality of rationing decisions. Armed with this information, educators can better guide clinicians toward choice options associated with favorable risk-benefit profiles.

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TERRY L. JONES, PhD, RN, is Assistant Professor of Nursing, Department of Nursing Administration and Healthcare Systems Management, University of Texas at Austin, Austin, TX.

Table 1.
Sample Characteristics

                                                         Median

Age (years)                         26-76   49   10.98      51
Work Experience (years)
  Nursing unit                      2-50    19   11.6       17
  Current unit                      <1-48   9     7.8        5

                                                    n        %

Gender
  Female                                           21       89
  Male                                             23       10
  Missing                                           2       <1

Ethnicity
  White                                           152       67
  Black/African American                           13        6
  Asian                                            20        9
  Hispanic                                         34       15
  Other                                             5        2
  Missing                                           2       <1
Education (155 RNs & NMs only)
  Diploma                                           7        5
  ADN                                              65       42
  BS/BSN                                           74       48
  MS/MSN                                            7        5
  Missing                                           1       <1
Employment Status
  Full-time nursing                               201       89
  Part-time nursing                                20        9
  Missing                                           5        2
Primary Work Shift
  8-hour days                                      10        4
  12-hour days                                     76       34
  8-hour evenings                                   5        2
  12-hour nights                                   59       26
  Other                                             5        2
  Missing                                          71       31

Public Health Regions
  High Plains (1)                                  13        6
  Northwest Texas (2)                              27       12
  Metroplex (3)                                    29       13
  Upper East Texas (4)                             22       10
  Southeast Texas (5)                              16        7
  Gulf Coast (6)                                   20        8
  Central Texas (7)                                27       12
  Upper South Texas (8)                            32       14
  West Texas (9)                                   16        7
  Upper Rio Grande (10)                            14        6
  Lower South Texas (11)                           10        4
Geographic Setting
  Metropolitan Service Area                       181       80
  Nonmetropolitan Service Area                     45       20
Work Setting
  Charge nurse without a patient
  assignment
    Yes                                           113       50
    No                                            108       48
    Missing                                         5        2
  Routine access to unlicensed
  assistive personnel
    Yes                                           184       81
    No                                             38       17
    Missing                                         4        2
  Magnet recognition status
    Magnet hospital                                43       19
    Pathway to Excellence
      hospital                                     56       25
    No Certification                               95       42
    Missing                                        32       14

Table 2.
Item-Level PIRNCA Scores

                       Frequency
                           of
                         Scores      % Scores       Mean
Nursing Activity          1-3          1-3         Scores

Timely response to        208           93          1.72
request/need (less
than 5 minutes)

Review documentation      184           82          1.55
by care team

Routine hygiene           193           85          1.52

Document all nursing      176           78          1.50
interventions/care

Patient teaching          184           81          1.48

Emotional or              184           81          1.45
psychological
support

Important                 187           83          1.41
conversation with
other team members

Evaluate the plan of      126           75          1.36
care *

Assist with needed        175           77          1.35
ambulation

Provide adequate          157           70          1.29
supervision of
delegated tasks

Routine skin care         171           76          1.27

Mobilize or change        172           76          1.24
patient position

Document initiation       162           71          1.21
or revision of plan
of care

Monitor patient           162           71          1.19
behavior

Timely assistance         172           77          1.19
with elimination

Document assessment       158           70          1.19
and monitoring
activities

Promote physical          169           75          1.14
comfort

Follow up on change       159           70          1.11
in patient status

Assist with feeding       153           68          1.11
patient

Important                 152           67          1.11
conversation with
patient about
discharge

Monitor patient           162           72          1.09
safety

Important                 146           65          1.06
conversation with
external agency

Prepare patients for      146           65          1.04
treatments

Change intravenous        147           65          1.03
sites or tubing

Change soiled linen       148           65          1.03

Monitor patient           144           64          0.96
physiological status

Change dressings          121           54          0.84

Provide wound care        123           54          0.80

Administer                100           44          0.62
medications

Adhere to infection        97           43          0.57
control guidelines

Administer enteral         77           34          0.44
nutrition

* RNs only

Figure 2.

Mean PIRNCA Scores with 95% Confident Intervals Across Public
Health Regions in Texas

High Plains (1)         0.83
West Texas (9)          0.83
Northwest Texas (2)     1.05
Gulf Coast (6)          1.06
Southeast Texas (5)     1.07
Metroplex (3)           1.13
Upper East Texas (4)    1.24
Central Texas (7)       1.27
Upper Rio Grande (10)   1.27
Upper South Texas (8)   1.32
Lower South Texas (11)  1.43

Note: Table made from bar graph.
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Author:Jones, Terry L.
Publication:Nursing Economics
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Geographic Code:1USA
Date:May 1, 2015
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