This document summarizes a study that aimed to estimate nursing facility (NF) resiliency based on complexity and emergency management (EM) plan adequacy. The study collected data through surveys of NF administrators in Florida between 2014-2015. A structural equation model was used to analyze the relationships between NF complexity, EM plan adequacy, and NF resiliency. The results found that staff confidence in the adequacy of the EM plan significantly contributed to estimating NF resiliency. However, more detailed longitudinal studies are needed to quantify how improvements to EM plans can strengthen future NF resiliency.

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Estimating Nursing Facility Resiliency for the Next Disaster
ABSTRACT
Objective—To estimate nursing facility (NF) resiliency by complexity and emergency
management (EM) adequacy.
Data Sources/Study Setting—Primary and secondary data from Florida nursing facilities
were collected between November 4, 2014 and January 19, 2015 through surveys and
datamining the 2012 CMS CASPAR and MDS 3.0.
Study Design—quantifiable measurements of NF staff KSAs were collected for a
multivariate analysis of the causal effects of NF complexity and EM plan adequacy upon NF
resiliency. A contingency model demonstrated three quantifiable constructs with correlational
effect that significantly supported the emergency management process in estimating NF
resiliency. Key variables were patient efficacy, workload, size, patient/nurse ratio, and staff
experience.
Data Collection/Extraction Methods—Primary data was collected from a randomized
sample (n=200) of Florida NF administrators (N=680). Surveys of twenty-five (25) questions
were completed by 102 of 200 Florida NFs.
Principal Findings—the prospective study found staff confidence in the adequacy of the
EM plan is significant (.82; p > .01) toward estimating NF resiliency.
Conclusions—it is possible to estimate NF resiliency contingent upon the NF’s
complexity and the adequacy of the EM process in preparing NF staff. However, more detailed
longitudinal studies are needed to quantify EM plan improvements in strengthening future NF
resiliency.

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Introduction
Healthcare professionals are credited with providing the best possible quality of patient
care during the response and recovery phases of disasters affecting their communities. In 2005,
property losses due to nationally declared disasters were over $115.5 billion dollars and totaled
1,256 disaster related deaths. Six years later the fiancial losses decreased to $23.8 billion with
1,019 souls lost (Hazards & Vulnerability Research Institute, 2012). Disaster research scholars
and healthcare legislators believe contingency planning saves lives and minimizes loss of
property. Therefore, emergency management (EM) plans are required for every healthcare
facility.
This research asked the question: “If emergency management (EM) plans are designed to
save lives and protect property, do EM plans also estimate NF resiliency?”
First, an agreed upon measureable definition was needed for resiliency. The Committee
on Increasing National Resilience to Hazards and Disasters recommends “ a good measure of
resilience…to identify priorities for improvement, determine whether resilience has improved or
worsened, or compare the benefits of resilience with the associated costs” (The National
Academy of Sciences, 2012, p. 10).
Resiliency as defined by the American Psychological Association is “the process of
adapting well in the face of adversity, trauma, tragedy, threats or even significant sources of
threat” (American Psychological Association, 2014, p. 14). For the purposes of this discussion,
the resiliency of a health care organization will be defined as the ability to change from normal
patient care operations to Crisis Standard Care(CSC) during disaster response and recovery
operations (i.e. power outage, tornado, or hurricane). In other words, nursing facility resiliency
can be a measurement of quality of patient care.

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When changing from normal operations to disaster operations, the quality of patient care
becomes the “ethical basis” for planning the allocation of disaster resources (Institute of
Medicine, 2009, p. 7). Healthcare disaster planners consider CSC as the goal for equitable
quality of patient care when resources are depleted (CDC, 2016, p. 186).
Therefore, the measure of NF resiliency in this research was defined as an estimate of
performance outcomes needed to maintain the predetermined level of CSC. The term NF
resiliency is defined in this study: as the capacity to absorb, cope and recover quality of patient
care from a disruption of normal operations.
An example of normal patient care operations is illustrated in the skilled nursing facility.
Nursing facilities (NFs) are considered among the most complex and therefore most vulnerable
healthcare unit within a system. These facilities face the greatest resiliency challenges because
NF patients are chronically ill with multiple comorbidities (i.e. dialysis, ostomies, and palliative).
Also, the nursing facility is exempted from mandatory evacuations because the frail patients are
already compromised with comorbidities and are extremely susceptible to decompensation,
especially when subjected to sudden changes within their routine care (Bascetta, 2006) (Eiring,
Blake, & Howard, 2012). Therefore, this study used the NF as the unit of analysis because it is
complex and requires more resilience than other organizations.
According to Fielder’s Contingency Theory, an organization’s outcomes are dependent
upon the “least preferred worker” implementation of internal processes and outputs (Ashby,
2007). The NF staff rely upon the internal process of written EM plans as instructions and
authorizations to regain pre-disaster CSC as a resiliency output. NF staff KSAs and the EM
processes correlate to NF resiliency. However, little research has been done to identify empirical
measures of organizational resilience.

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Thus, for this study contingency theory is the framework for testing the hypothesis: “NF
staff support of EM plan can estimate NF resiliency.”
This study supports the idea that NF resiliency is contingent upon three contextual
elements: 1) the type, extent and duration of the disaster (i.e. tornado F4 damage and three days
without utilities); 2) NF patient complexity (i.e. ADL, acuity and comorbidities); and 3) the NF
staff knowledge, skills and attitudes regarding EM written procedures.
The rationale for using Florida as the context for the study was based upon Florida’s
historical experience with natural disasters and the state’s popularity as a retirement destination.
This provided a cultural mix of skilled nursing facilities and nursing staff exposed to the perils of
hurricanes, wildfires, and related economic shifts.
Florida legislation requires licensed NFs to have EM plans designed with all
contingencies for every type of hazard, including hazardous chemical spills and acts of terrorism
(Florida Statute 59A-4, 2012).
Methodology
The methodology for this study needed a way of capturing the complexity of the NF
patients, staff KSAs, EM plan adequacy and NF capacity for resiliency. So this research study
used three measurement models nested within a modification of the healthcare sector’s standard
for performance measurement.
A covariance structure model or structural equation model (SEM) was modified to
illustrate the causal paths and to capture the correlational relationships between the NF staff’s
confidence in the adequacy of an EM plan as a tool for estimating NF resiliency.
Contingency models traditionally demonstrate causal paths of structure upon internal
processes and subsequent causal paths upon outputs/outcomes (S→P→O). This methodology

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provided a way to test the research hypothesis: “NF complexity and EM adequacy can be used to
estimate NF resiliency.” Put another way, NF resiliency is contingent upon available resources
and the NF staff’s ability to obtain more through an adequate EM process.
The methodology for this study used three measurement models nested within a
modification of the healthcare sector’s standards for measuring performance. The complexity of
the NF structure (S) correlated with the adequacy of the emergency management (EM) process
(P) in estimating the NF staff’s capacity to maintain CSC as the NF resiliency outcome (O).
This model also allowed for multivariate analysis of large numbers of first-order
exogenous indicators into the second-order latent endogenous indicators for ease of data
management.
Therefore, this research methodology provided the ability to quantify the NF capacity to
absorb, cope and recover quality of patient care from a disruption of normal operations.
Study Validity
Sample size is essential to protecting internal validity of the data. This proposed research
measurement model for the unit of analysis (NF) that is so complex with patient comorbidities
required a robust method for determining a range of sample sizes.
There were thirty-three (33) indicators within the hypothesized composition of three
latent constructs from a population of 680 possibilities. Initially, surveying the entire population
(N=680) seemed impractical and not cost effective. There was also the possibility of a Type I
error of rejecting a true hypothesis (i.e. resiliency is estimated by NF complexity and staff KSA
of EM Plan) (Bickel, 2007).
Therefore, a standard formula of Cronbach α set at > .8, provided more precision in
determining effect significance. An Effect Size Calculator designed by Dr. Daniel Soper for

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hierarchical multiple regression was chosen to confirm a valid minimum and maximum sample
size (n = 75 < 200; p > .05). This calculator considered multiple latent endogenous constructs
that required individual regression correlation analyses. A sample range of sizes protected the
internal validity of the study with respective levels of accuracy in data collection.
In other words, effect size calculations provided a simulated statistical methodology that
considered three latent constructs: NF complexity, EM plan adequacy, and NF resiliency. These
three latent constructs nested within one final covariance measurement model. Calculations for
sample size were done with proper simulation techniques and power analysis.
The list of assumptions used as threshold parameters are identified and listed here:
Anticipated effect size = .3
Desired statistical power level = .8
Number of latent (unobserved) variables = 19
Number of indicator (observed) variables = 14
Probability or p-value (rule out Type I error) = .05
The recommended optimal sample size was 200 with a minimum sample of 75
participants needed to detect the medium effect (Soper, 2012).
Data Collection
Descriptive data of the nursing facilities in Florida were collected from the Florida
Association of Healthcare Agencies (FAHCA) 2012 database and cross referenced with 2012
CMS data. Descriptive characteristics of the NF and the exogenous variables X(i) were gleaned
and cross checked with other pertinent data into one manageable dataset for regression
correlation analysis. See Table 1 Confirmed Resiliency Variables.
A tracking method was needed to correlate data from multiple reports. So, the CMS
provider number was embedded into the facility’s KSA survey. This CMS provider number was

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used as a reference point among other reports until all facility related data were collected. Then
the CMS provider number was replaced with an anonymous random case number prior to the
KSA survey distribution.
The University of Central Florida Institutional Review Board approved the KSA survey
instrument and the informed consent document for this research. The survey questions were
adapted from a 2009 nursing facility research grant project conducted by the University of South
Florida for the John Hartford Foundation (Florida Health Care Education and Development
Foundation, 2008, pp. 14-20). Each nursing facility survey was addressed to the administrator
because they are the contact person for local emergency response agencies.
Surveys were distributed only after receiving written consent or approval from each of
277 NF administrators. Then the consenting cases (277 of the 680 contacts) were de-identified of
any personal information that connected the survey participants to their respective facilities.
Then 200 NFs were randomly selected from the de-identified database of 277 pre-screened
participants.
The KSA survey questions were distributed among nursing facility employees and to
minimize social likeability bias, two methods were used to protect the participants. First the
twenty-five (25) survey questions were distributed in a randomized sequence with a 5-point
Likert scale of responses: “none,” “less than enough,” “enough,” “more than enough,” and “do
not know.” Secondly, the KSA survey were de-identified of any personal contact information
and distributed electronically as unique one-time use links through Qualtrics with an “opt out”
option.

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KSA survey distribution began November 4, 2014. Links to the surveys were closed
January 19, 2015. Thus survey respondents had within ten weeks to reply. Three reminders were
emailed in two week intervals until the study was closed.
It was important that the survey timeframe occurred during the last month of hurricane
season and the last quarter of the NF planning cycle.
The primary KSA survey data were combined as aggregated numbers with the secondary
data collected from the Centers for Medicare and Medicaid (CMS) CASPAR and MDS
databases.
Confirmatory Factor Analyses
A descriptive analysis, ensured that the study sample (n= 200) was a true representation
of the entire NF administrator population (N= 680) within the state of Florida. Consenting
participants came from thirty-one of the sixty-seven counties in Florida. The majority of the 102
completed KSA surveys came from Orange (15.7%) and Pinellas (12.7%) counties.
The first step was to run a confirmatory factor analyses (CFA) using SPSS 22.
Confirmatory factor analysis (CFA) identified the first order elements for each of three latent
constructs: the NF complexity of structure (S), the adequacy of EM process (P), and the expected
NF resiliency outcome (O).
The Confirmatory Factor Analysis (CFA) included a Factor Reduction treatment of the
39 exogenous variables considered for NF complexity(S) into eight (8) easily manageable first-
order factors (Xi) and ten (10) indicators for resiliency (Yi).
These eighteen (18) variables were scientifically significant to the structural equation
model (SEM) of and are described with each data source in Table 1 Confirmed Resiliency
Variables.

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The Factor Reduction process provided flexibility as regression analyses sorted
collinearity of the proposed variables into three latent endogenous constructs: complexity η1;
adequacy η2; and resiliency η3.
The first-order factors were reduced into second-order components for the latent
construct indicating NF complexity η1. These NF complexity elements were respectively labeled
as: patient acuity (1), workload (2), and administrative strengths ( See Figure 1 for the
Model for Estimating NF Resiliency.
The following section discusses the results of the CFA of the measurement model for the
latent endogenous construct for the process: EM adequacy (P). The EM plan adequacy
indicators chosen for this study are industry standards based upon the AHCA and CMS criteria
for NF licensure. These healthcare indicators were also used in an earlier research study
conducted by the CDC and the University of South Florida through a grant from the Hartford
Foundation (Eiring, Blake, & Howard, 2012).
Three of the nine proposed indicators for EM plan adequacy (P) did not meet the
predetermined threshold for significance (< .30). Therefore, six indicators remained as first
order factors: alternate facility (Y1), primary communications (Y2), written transportation
agreement (Y3), additional disaster staff (Y4), back-up power (Y5) and medication reserves (Y6).
The next paragraph discusses the factor analysis results of indicators scientifically significant for
estimating NF Resiliency (O).
Confirmatory factor analysis (CFA) of resiliency indicators has never been done for a
skilled nursing facility. However, the CFA of primary data collected from this study’s KSA
survey did provide four quantifiable indicators for NF resiliency. The four strongest factors

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were: travel caregiver to accompany patient (Y7), knowledge of travel time (Y8), knowledge of
EM resources process (Y9), and authorization to activate the EM resources system (Y10).
Goodness ofFit
Factor loadings were checked for significant effects (direct and cumulative) of indicators
on latent constructs. The larger the loading, then the more effect of that indicator on correlated
constructs. In other words, the larger the effect of NF complexity (S) indicators such as patient
acuity, workload and admin strengths then the more effect is possible on the correlated construct
of EM plan adeqaucy (P) as a process and then a cumulative effect of NF complexity plus EM
plan adequacy (S+P) on the correlated construct of NF resiliency (O) outcomes!
The t-value or Critical Ratio (C.R.) range was larger than 1.96 or less than -1.96. This
ensured that there was a good model fit and that the stastically significant effect remain between
.05 and .09 (Byrne, 2010).
Consideration of the importance of an indicator to the study was determined when
estimating the sample size. The indicator threshold established at .30 meant that any indicators
above .30 and below .90 remained within the final contingency model for measuring and
ultimately for estimating NF resiliency.
Other measures for model reliability were likelihood ratios: CMIN (x2/df), goodness of
fit index (GFI), adjusted goodness of fit index (AGFI), parsimony goodness of fit index (PGFI),
root mean squared (RMS), root mean squared equity adjustment (RMSEA) and Tucker-Lewis
index (TLI). Pearson distribution coefficient was also considered for normal curve distribution as
another indication of goodness of fit (Spatz, 2008; Wan, 2002).
Some analysts consider the Hoelter critical N of .05 as confirmation of a significant
sample size. Hoelter rejected models with more than 200 participants because results can be

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misconstrued as chi-square moves from nonsignificant to significant in samples greater than 200.
Instead, Hoelter believed sample sizes between 75 and 200 are acceptable confirmation of the
chi-square goodness of fit test (Pallant, 2007). However, some scholars continue to ignore
Hoelter and prefer Cronbach’s α (alpha) to protect the internal validity of data.
Cronbach α ensured the data collected were reliable with the probability of being
repeated in future studies. The acceptable minimum threshold for this study’s Cronbach’s alpha
was set at .8 as recommended in the literature (Bickel, 2007; Pallant, 2007). However, the
findings were closer to 1 and therefore the study more robust than expected before data
collection.
As the last reliability step, the modification index (MI) list of values were used as
recommended adjustments to the model for a parsimonious fit. The MIL identified measurement
error correlations that were too high and were considered redundant among the latent constructs
(Bickel, 2007). Thus the original 39 varaibles became the final 18 varibles in the final sturctural
equation model for estimating NF resiliency within a contingency framework.
Findings
Significant indicators were compared between the FL NF population (N=680) and the NF
sample (n=200). See Table 2 Descriptive Statistics for Population Sample.
These six elements for sample integrity comparisons were: number of dialysis patients
(DIALYSIS); the number of patients with ostomies (OSTOMY); the mean of patients requiring
assistance with daily living (ADLINDEX); the mean of patient acuity (ACUINDEX); the
number of times changes were made in ownership within the previous twelve months
(CHG_OWN); and the size of the NF as determined by the number of certified beds
(CERTBEDS).

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 DIALYSIS findings— Facilities had an average of 2 +/- 2 patients on dialysis.
The highest number in one facility was 15 dialysis patients.
 OSTOMY findings—Ostomy patients per facility were higher in numbers with an
average of 5 +/- 7 patients. In 2012, according to the CMS data, one of the Florida
NFs in the sample has the capability to house 42 ostomy patients (CMS, 2013)!
 ADLINDEX findings—Assistance with daily living (ADL) Index is 5 units to 12
units per skilled nursing facility. It is based upon the overall patient ADL status as
reported to CMS online through CASPER. In this study the mean ADL Index for
patient severity among the NF population (N=680) was 10.42 +/- 1.21. The NF
study sample (n=200) mean for the ADL Index was 10.47 +/- 1.63 units. ADL
Index findings were skewed highest for NFs providing Hospice care (CMS,
2013).
 ACUINDEX findings— The Acuity Index measures patient cognitive abilities
and ranges from 0 to 19 units. Similarly to the ADL Index, the patient acuity
index is based upon the overall patient status of the facility. The study sample
(10.77 +/- 1.8) was a good representation of the population (10.70 +/- 1.3).
 OWNERSHIP findings—The 102 survey participants included nineteen (19)
facilities not affiliated with a healthcare system and eighty-three (83) facilities
were affiliated with large corporations. Change in ownership did not occur among
any of the NFs within the twelve months prior to this research.
 CERTBEDS—Facility size is customarily represented by the number of beds
certified by Medicaid to accept patients. The average size of the sampled NFs was
116 beds.

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GENERALIZABILITY—Findings confirmed that the sample (n=200) represented the
research population (N=680). Findings from 102 participant surveys (51% response) were
generalizable and testable for future studies of NF complexity and resiliency. See Table 3 for the
parameter estimates (standardized and unstandardized regression weights) for this study’s
confirmed variables X(i) and Y(i).
Empirical evidence demonstrated the significance of EM plans in estimating resiliency
outcomes. For example, staff confidence in using cell phones and texting as communications
was one of the strongest indicators for plan adequacy with a correlation coefficient of .89 at a
confidence level of .95, p < .01.
The majority of the NH administrators surveyed (.92 +/- .27) required staff to use text
messages through cell phones during disasters. Other EM plan elements displayed significant
relevance in association with resiliency capabilities as follows: Transportation agreement (.499,
p < .01), Additional staff (.573, p < .01), back-up power (.322, p < .01) and medications (.485, p
< .01).
However, the findings may not be generalized to organizations that have fewer
regulations, because the surveyed NFs were isomorphic, and adhered to regimented licensure
criteria. EM plans mimic other EM plans among other NF units within the similar corporate
healthcare systems.
The study findings specific to the exogenous variables for NF complexity of structure (S)
begin in the next paragraph. What follows is a discussion regarding findings related to the other
two latent endogenous constructs: EM plan adequacy (P) and NF resiliency (O).
First consideration was the context of the nursing facility. The context of the nursing
facility included a mixture of patients described as frail, elderly, chronically ill, with disabilities

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and comorbidities. Three control variables for protecting the homogeneity of the sample were:
license status, percentage of elderly in the county where the NF was located and the urban/rural
service area.
Besides patient acuity and chronic health conditions, some social science scholars believe
cultural barriers can also influence patient and staff dynamics within the context of healthcare
(Azlina & Jamaluddin, 2010) (Drabek & McEntire, 2002). Group diversity affects community
members’ expectations and can either lessen or enhance the organization’s problem-solving
experiences. Issues of trust and embarrassment create additional disaster resiliency problems as
the human phenomenon of not appearing needy and therefore not reaching out for assistance
(Rivera, 2012).
There may have been a cultural barrier between the administrators of St. Rita’s Nursing
Home and local emergency managers when 34 patients died during Hurricane Katrina in 2005
(Dosa, et al., 2010). Another example of a cultural barrier can be observed through differences
in language and personal rationale. The ability to overcome obstacles may be derived from the
aspect of an internal culture through patient expectations and NF staff qualifications. During
times of disaster, the nursing facility patients expect to use the language that they are most
familiar and are comfortable. This is more probable during times of emotional distress. Thus,
communication barriers are a consideration in EM plans. The number of patients using another
primary language are reported each quarter to CMS as Non-English. Therefore, in this study, the
variable NON_ENG was used as a proxy indicator for cultural diversity within FL NFs.
The count used for this study came from the last quarter of the 2012 Minimum Data
Statistics (MDS) database. Facilities in the survey sample with Non-English speaking patients

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averaged 4 +/- 7 patients. This was significant to organizational complexity because translators,
interpreters, or multi-lingual assistance may be needed to provide CSC quality during a disaster.
Another descriptive statistic was the organization’s dependence upon Medicaid for
revenue. The indicator PCTMCAID, demonstrated the percentage of Medicaid reimbursements
for each facility. Of the 200 facilities in the sample, only 4.9% +/- .22 did not have Medicaid
patients while the other 95.1 % +/- .22 received Medicaid compensation. This may be because
the sample data came from the CMS database. As a socioeconomic indicator, this confirmed the
NF sample is composed disproportionately of Medicaid patients. The patient care population in
the State of Florida is 55.7% (+/- .8) Medicaid.
The percentages of beds occupied in each facility were identified by the variable,
OCCUPANCY. In the NF sample, facilities averaged 87% +/- .15% of available beds filled in
2012. This revenue indicator affected workload levels of staffing hours and required resources.
Physical therapy per patient per day (PTHRD) was extremely small when compared with
other healthcare services. Perhaps due to the endurance levels of frail patients and the frequency
of therapy sessions offered. In other words, the therapist may not be meeting with individual
patients each day, but in groups or as weekly sessions. The CASPER reports for the survey
sample indicated the average PTHRD was .13 +/- .09 hours per day (or 5 to 12 minutes a day).
This variable was ruled out in the correlation regression analysis as less significant and was
eventually removed from the structural equation model. However, PTHRD was included in the
variable for total staff/patient contact hours (TOTHRD).
The dataset also provided the Total Staff hours per patient per day (TOTHRD). The
average among the survey sample was 4.33 +/- .98 hours (or 260 +/-59 minutes). Both
CNAHRD and TOTHRD were assumed to have a strong correlation because CNA’s provide the

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majority of TOTHRD patient contact hours. Now, that we have discussed the nursing hours, we
will describe the staff ratings found within the quality assurance reports included in 2012
CASPER data.
The NF facility staff (STAFFRATING) are evaluated on a scale of 1 (low) to 5 (high).
All patient care services and administrative staff are included. STAFFRATING of the research
sample averaged 3.45 with a deviation +/- 1.02. The highest percentile rating was 4 and 58.8% of
the sample were rated 4 while 5.9% were rated a 5. So approximately 64% of the sample NF
staff were rated above average (3).
Registered nurse ratings (RNRATING) averaged 2.96 +/- 1.12 with 56% of the sample
ratings at 3 or above. Only 32% of the RN staff were rated below 3. Staff ratings are only one
measure of the organizational complexity within a nursing facility; another complexity measure
is the nurse to patient ratio.
The nurse/patient ratio average was 4.29 +/- .98 or roughly 4.3 hours per day per patient
per licensed professional. This statistic was derived from self-reported times collected from each
certified nurse, licensed nurse and registered nurse per facility.
Delimitations
There was concern regarding social desirability bias affecting the resiliency outcomes.
Since the 1960’s some scholars believe there are two paths of social desirability bias. One is self-
deception and the other path is other-deception. Self-deception occurs whenever the participant
chooses an answer based upon their belief that socially unacceptable characteristics should be
ignored. Therefore, study participants may choose to provide a preferred or morally acceptable
answer. Other-deception bias is evident whenever participants provide inaccurate data to impress

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an observer. Either type of bias is intended to protect that participant’s self-image. Therefore, it
was imperative that the validity of the results from the KSA survey be protected.
It was important to keep in mind that not all data in this study was cross checked among
other data sources. The self-reported primary data were anonymously linked with other
secondary data, therefore the need for social approval, various response styles, evaluation
apprehension, pre-survey effects, and social desirability bias were minimized as aggregated
results (Azlina & Jamaluddin, 2010; Nederhof, 1985).
Conclusions
In conclusion, the research question: “If emergency management (EM) plans are
designed to save lives and protect property, do EM plans also estimate NF resiliency?” can be
answered “it depends.” This study supported a significant causal path (β = .82, p < .001) toward
NF Resiliency and thus contingent upon the adequacy of a written EM plan. It can further be
deduced that estimating NF resiliency depends upon the facility’s capacity to implement the
Critical Standards of Care (CSC) post-disaster. More detailed comparisons need to be made
between NF and other complex healthcare organizations that require EM Plans.
The NF working environment is so highly regulated that it may be concluded that this
study’s findings were subject to isomorphic organizational behavior and therefore the data were
prone to suffer from “regulatory capture.”
Highly complex organizations such as a skilled nursing facility may present with highly
correlated characteristics and often adopt routines learned from other similar organizations
(Drabek & McEntire, 2002; Salamon, 2002). In this study, regulatory capture could explain why
the bivariate correlations met or exceeded the maximum threshold (.9) for significance among
the first-order factors within the complex NF structure (S). Some of the greater than .9

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regression correlations in this dataset were not interpreted as a multicollinearity issue because
they were attributed to regulatory capture.
Examples of these bivariate correlations were ostomy patients and patients requiring
dialysis. These licensed services may have construed the increased demand for licensed
practitioners (the regulated workforce) into a negative effect of diminishing returns on
investment (ROI) while simultaneously increasing a positive effect of elevating ratings of
licensed staff. This phenomenon complicated efforts to identify which characteristics between
the facilities were responsible for resiliency, as the EM plan followed a regulated design criteria.
This multivariate non-experimental research incorporated only the most significantly
related indicators extracted from three nested measurement models. It can therefore be
concluded that structural equation modeling (SEM) simplifies traditional analysis perspectives
(Byrne, 2010, p. 17).
It can be concluded that it is possible to create a methodology for estimating NF
resiliency as CSC performance during and after a disaster. However, this is only one
recommended method for estimating resiliency.
Other techniques may originate among the emergent disaster research collaborations
developing across disciplines. Healthcare providers, emergency management practitioners and
public health systems conduct annual disaster training exercises and disaster after action reports
in anticipation of the next disaster. Future implementation studies are needed for more precise
resiliency estimations on three levels: individual, organization and community.
So, there remains a continuing need for future longitudinal studies of ongoing
improvements in the emergency management planning process. This is only one alternative in
estimating a nursing facility’s future resiliency capabilities within a disaster environment.

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Table 1 Confirmed Resiliency Variables
Second
Order
First Order Variable Database Code Factor
Description
Data
Source
CON-
TROLS
COUNTY
%
elderly/county
CENSUS
RURAL
Rural or
Urban
CASPER
NF
Complexity
1
Patients
Acuity
1
X1 ADLSCORE
Patient
severity
CASPER
X2 ACUINDEX Patient acuity CASPER
X3 CNAHRD
CNA time
with patient
CASPER
Workload
2
X4 LPNHRD
LPN time
with patient
CASPER
X5 OCCUPANCY
Percent beds
occupied
CASPER
Administrative
Strengths
3
X6 STAFFRATING Staff Rating CASPER
X7 RNRATING Nurse Rating CASPER
X8 NURSERATIO
RN per
patients
CASPER
Plan Adequacy 2
Y1 ALTFACILITY
Evacuation
point
KSA
Survey
Y2 PRIMCOM
Cellphones &
text
KSA
Survey
Y3 TRANS
Written
agreement for
disaster
transport
KSA
Survey
Y4 ADDSTAFF
Additional
staff for
disaster
KSA
Survey
Y5 POWER
Back-up for
outages
KSA
Survey
Y6 MEDS
Reserves for 3
days/patient
KSA
Survey
NH Resiliency 3
Y7 TRAVCAREGIVER
Evacuation
staff w/patient
KSA
Survey
Y8 TRAVTIME
Know time
needed to
travel out of
harm's way
KSA
Survey
Y9 ESSPROC
EM
Procedures
KSA
Survey
Y10 ESS
EM
Authorization
KSA
Survey

22. 22
Table 2 Descriptive Statistics for Population Sample
FL NH Population (N=680)
Randomly Selected Sample
FL NH (n=200)
Completed KSA Surveys
(n=102)
Min Max Mean S.D. Min Max Mean S.D. Min Max Mean S.D
Dialysis 0 13 2.09 2.26 0 38 6.5 2.33 0 15 2.26 2.33
Ostomy 0 52 5.06 6.69 0 41 5.15 7.14 0 41 5.15 7.14
ADL
Index 6 16 10.42 1.21 10 12 10.32 1.63 0 16 10.47 1.63
Acuity
Index 6 19 10.70 1.30 10 12 10.81 1.79 0 19 10.77 1.79
Owner
Change 0 10 2.03 2.13 1 1 1 0 1 1 1 0
# of
Beds 15 462 120.83 49.79 20 300 120 44.52 20 300 116.44 44.42

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Table 3 Parameter Estimates and Regression Weights for NF
SRW URW S.E. C.R. P
Plan_Adequacy .143 .479 .346 1.383 .167
NH_Resiliency ( .819 .792 .119 6.650 ***
Patient_Acuity(F1) .282 1.879 .775 2.429 .015
Workload (F2) 1.051 1.000
Admin_Strengths (F3) .802 11.916 2.434 4.895 ***
ADLSCORE (X1) .918 1.000
ACUINDEX (X2) .673 2.305 .978 2.357 .018
CNAHRD (X3) .893 7.630 1.309 5.827 ***
LPNHRD (X4) .714 2.691 .516 5.214 ***
OCCUPANCY (X5) .504 1.000
STAFFRATING (X6) .679 .594 .091 6.525 ***
RNRATING (X7) .393 .372 .110 3.385 ***
NURSERATIO (X8) 1.189 1.000
ALTFACILITY (Y1) .885 1.000
PRIMCOM (Y2) 1.000 1.022 .065 15.618 ***
TRANS (Y3) .499 .753 .136 5.536 ***
ADDSTAFF (Y4) .573 .880 .134 6.593 ***
POWER (Y5) .322 .329 .098 3.362 ***
MEDS Y6) .222 .117 .051 2.269 .023
TRAVCAREGIVER
(Y7)
.703
1.000
TRAVTIME (Y8) .594 1.016 .149 6.801 ***
ESSPROC (Y9) .441 .789 .210 3.756 ***
ESS (Y10) .667 .871 .159 5.485 ***

24. 24
Figure 1 Model for Estimating NF Resiliency