EXAMPLE Benchmark – Staffing Matrix and ReflectionPerformanc

*EXAMPLE* Benchmark – Staffing Matrix and Reflection
Performance management of a healthcare institution depends on
the efficient management of human resources. Having the
availability of qualified personnel, productively working in the
appropriate areas enhances both outcomes and quality of patient
care (Thériault et al., 2019). The paper aims to discuss the
benefit of adopting a staffing matrix in a healthcare setting,
describes a staffing matrix plan and the changes that might be
required compared to the patient census, and creating a variance
report to reallocate resources according to needs.
Importance of a Staffing Matrix
A staffing matrix is an essential and indispensable aspect of the
allocation and resource utilization within the healthcare setting.
Numerous units adopt various staffing matrices to certify there
are no replications within the unit’s employees and resources.
The staffing matrices offer the advantage of providing a clear
vision concerning the management of the unit’s personnel. Also,
the requirements of the unit together with the policies of
financial management and the allocation and resource
utilization ((Dagestad & Grassley, 2019). Staffing matrix assists
in determining and assigning the daily patient care and duties
required in a nursing unit. It offers a clear image of the working
personnel schedule to permit for an appropriate mix of nursing
care to patients’ requirements instead of being reactive for each
shift (Johnson-Carlson et al., 2017).
A staffing matrix assists in budget determination associated
with human resources and allocation of finances and assists in
preventing funds wastage. Financial principles are applied to
obtain a more robust understanding of the allocation of the
unit’s finances. Another importance of the staffing matrix is
that it ensures enhanced patient care quality by providing highly
skilled trained nursing personnel that equals patient’s
understanding.

Staffing Matrix Description
The staffing matrix shown in the provided excel template lists
the daily census for a week and achieves a 90% occupancy
percentage. With staffing established at twelve hours shifts, the
Full-Time Equivalent (FTE) direct nursing coverage aspect for a
twenty-four hours duration is 4.7 nursing FTEs. Fixed staffing
for this section comprises the manager, nursing in charge, and
the health unit coordinator. Therefore, unit staffing will be
based on the patient occupancy percentage, hours per patient
day (HPPD) based on the patient understanding in this context
approximated at 360, and a nurse-to-patient ratio based on
patient acuity and occupancy. The initial five days of the matrix
reduce the daily tally by one patient per day, commencing with
30 patients and reducing by one to 26 patients. Staffing on these
five days could exhibit the requirement of one health unit
coordinator (HUC), seven registered nurses (RNs) per shift, and
three certified nursing assistants (CNAs). Skill blend is not a
problem for the matrix since the primary nurses are RNs. The
matrix will enable a nurse-to-patient ratio of one registered
nurse to five patients (1:5), one RN as a resource and for patient
admissions, release, and transfers (ATDs), and one CAN to ten
patients (1:10). The remaining two days of the matrix on the
patient a day reduces to 25 and 24 correspondingly. Staffing for
the two days will result in a decline in RN and CNAs handling
to six and two, respectively. The staffing decline will level the
occupancy rate and continue to permit the same RN staffing
ratio.
The usage of the staffing matrix should enable improved patient
safety by ensuring adequate staffing and an additional increase
of RN personnel. The RN will help when the need arises in the
unit and allow the primary care RNs to remain on the unit
instead of dealing with patient transfers. The staffing matrix
improves staff morale and job satisfaction by minimizing the
nurse-to-patient ratio. It will ensure that the nurse doesn’t
consider they are being stretched to their limits. Hence, it can

lead to colossal personnel retention, overcoming the need for
agency nurses that will incur the unit a substantial budgetary
cost. The financial management principles to design the matrix
involves staffing (number of staff needed per day), patient
capacity, and utilization in terms of HPPD.
Staff Adjustments
Variations in patients’ numbers need changes to the staffing
matrix. Patient census happens at midnight each night to reflect
the minimal patient activity. Full patient bed capacity requires a
single in-charge nurse, seven RNs, one resource RN, three
CNAs, and one HUC. Staffing will differ depending on the daily
patient acuity and number. To sustain the nurse-to- patient
staffing ratio, a reduction of five patients will decline the RN
personnel by one nurse. The nurse who isn’t required for the
day might be shifted to another section or in charge of calls if
patient acuity is required. Adjustments in inpatient acuity might
be a patient on extracorporeal membrane oxygenation (ECMO)
that would need a ratio of one nurse to two patients.
Budgetary issues also influence the staffing matrix. A minor
patient census could demand a decline in staff or floating nurses
to other sections or placement of nurses on-call when necessary.
A nurse placed on-call approximately earns $5 per hour while
receiving calls and earns time and a half when called in. such
scenarios have two side effects on the budget. 1) personnel are
paid for not going to work, meaning the funds coming from the
budget minus productivity to account. 2) If the staff reports to
work, they no longer earn at the base rate but are paid at a time
and a half rate. It indicates that a nurse is usually paid $33 per
hour, and currently, they will earn $49.50 per hour minus
formerly accomplishing their forty hours to attain over time.
Reallocation of Resources Based on Staffing Variance
Restructuring of personnel and alteration for variances are
usually essential to continue within the budgetary limits for
staffing. The initial step to make when encountering

exploitation of FTEs is the evaluation of the staffing matrix to
guarantee the in-charge nurse compliance with the unit plan for
staffing to certify appropriate staffing resources are utilized
based on the patient acuity and census. Personnel utilization for
the suitable role should be reviewed since an individual could
not desire an RN in the role of a sitter. In contrast, it will be
more sensible to allocate the duty to the CAN. Another section
that should be reviewed is the employee expenditure report.
Actual performance should mirror the proper usage of HPPD
over time. Direct care that is a surplus of HPPD might indicate
the patient acuity is more significant than the original budget or
nursing care isn’t being delivered correctly. Focus on the unit
of service could also be needed. An upsurge or decline in
inpatient days should reflect an effective incline or decline in
personnel hours and expenditures (Penner, 2017).
Conclusion
Patient requirements and a balance of unit resources are crucial
in the management of the staffing matrix. The nurse manager
should balance the needs of each staff member, nurse to patient
ratios, and suitably expert staff, and budget limits. Since all
these factors might lead to frustrations, the development of a
strong staffing matrix and continuous review for
References
Dagestad, A. J., & Grassley, S. (2019). Embracing Change by
Moving Forward With an Activity-Based Staffing Matrix.
Journal of Obstetric, Gynecologic & Neonatal Nursing, 48(3),
S73.
Johnson-Carlson, P., Costanzo, C., & Kopetsky, D. (2017).

Predictive staffing simulation model methodology. Nursing
Economics, 35(4), 161.
Penner, S. J. (2017). Health policy and future trends. In
Economics and Financial Management for Nurses and Nurse
Leaders (3rd ed., Ch 15).
Thériault, M., Dubois, C. A., Borgès da Silva, R., &
Prud’homme, A. (2019). Nurse staffing models in acute care: A
descriptive study. Nursing open, 6(3), 1218-1229.
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Introduction:
Starting around 1972, Computed Tomography is consistently
being created phenomenally, eminently with the assistance of
software engineering, which has permitted making exceptionally
exact diagnostics. CT advancement went through a few phases,
from the model of Hounsﬁeld to going through the consecutive
and helical modalities. This advancement made CT a vital
assessment in radiology (Kanne & Lin, 2018). In any case, this
radiological strategy is the most lighting contrasted with
different procedures; it can convey a portion of 50-500 times
more prominent than a standard radiological assessment. Some
patients can have life-altering effects after having a CT scan,
but others oppose having the scan done, due to cancer risk.
Studies have shown that the scan has a low risk of cancer-
causing agents in the body (Pontone, G). To assess the danger to
patients from CT checks, a gauge of the dose delivered to the
skin and organs of a patient is fundamental. A need in this way
exists to decide proper dosimetric amounts, for example, the
organ dose and peak skin dose (PSD) (De las Heras).
The Federal Drug Administration (FDA) states that the Peak
Skin Dose is the “highest radiation dose accruing actually at a
single site on a patient’s skin.” Knowing the appropriate highest
dosage is vital so that no harm is caused to the patient. The

United States has regulated that the” fluoroscopic system
provides a display of the irradiation time, dose rate at the
interventional reference point during irradiation, and the
cumulative dose for the procedure upon completion of
irradiation” (Pontone, G). In preparation for actual patients,
technologists and physicists would revert to the manufactured
dose estimation which is called the Computed Tomography
Dose Index (CTDI). The CTDI is generally utilized for quality
control including the radiation output of CT machines.
Specifically, the volume CTDI is shown on the control center of
all CT machines and is promptly accessible to the administrator.
In any case, the CT Dose Index (CTDIvol) was originally
designed as an index of dose associated with various CT
diagnostic procedures, not as a direct dosimetry method for
individual patient dose assessments.
Moreover, CTDIvol is reported in two units: a 16-cm phantom
for head exams or 32-cm phantom or body exams. The
relationship between the CTDIvol and airiest dose depends on
various factors, two of which are the patient size and
composition. CTDIvol is displayed on the console of CT
scanners, and it gives genuine estimates of the dose being
delivered to patients and can serve to approve Monte Carlo
recreations (Jones, A. Kyle). Specifically, estimating Peak Skin
Dose is ideal since it is a surely known dosimetric amount that
directly identifies with radiation-incited skin wounds. Besides,
estimation estimates of PSD values, utilizing appropriate
phantoms can without much of a stretch be made across all
types of CT units and scan protocols accessible in clinics (Tack
& Gevenois, 2018). This is significant for comparing doses for
a similar CT examination in different facilities, which can
change fundamentally. More recently, modifications to the
original CTDI concept have attempted to convert it into to
patient dosimetry method, but have mixed results in terms of
accuracy.

Nonetheless, CTDI-based dosimetry is the current worldwide
standard for estimation of patient dose in CT. Therefore,
CTDIvol is often used to enable medical physicists to compare
the dose output between different CT scanners. Also, since
CTDIvol estimates the patient's radiation exposure from the CT
procedure, the exposures are the same regardless of patient size,
but the size of the patients is a factor in the overall patient's
absorbed dose (SSDE). The size-specific dose estimate (SSDE)
is measured in mGy, and it is a method of estimating CT
radiation dose that takes a patient's size into account.
From a radiation protection point of view, determining the
maximum dose delivered to the skin would allow deriving
quantities that can be compared with dose reference levels set
by national and international standards. The most important
outcome from a radiation safety perspective is evaluating if a
radiation injury had occurred quickly (NCRP Report 116.) In
this research, the peak skin dose delivered to a patient was
estimated experimentally by measuring the dose delivered to the
surface of the NEMA phantom and 32 cm CTDI phantom using
external dosimeters. These dosimeters will provide PSD values
for a given protocol and its related CTDIvol. From this, a
relationship can be evaluated between both quantities. The aim
of this project was to test the hypothesis that the size-specific
dose estimate (SSDE) has a sufficiently strong linear
relationship with PSD to allow direct calculation of the PSD
directly from the SSDE.
Materials and Methods:
The measurements were performed with a Siemens 64 slices,
Biograph mCT. A comparison was made between the CTDIvol
value displayed on the CT console and the measured CTDIvol
value using the AAPM protocol. For every examined scanner,
the CTDIvol was obtained from scans in an axial mode for head
scans and helical mode of the routine pelvis, cervical spine,

abdomen, and thoracic scans using the scan parameters as
shown in Table 1. The corresponding CTDIvol displayed on the
console was recorded as shown in Table 1.
Peak Skin Dose was estimated by using Nanodots dosimeters
(International Specialty Products, Inc., Wayne, NJ, USA) which
have optically stimulated luminescence (OSL) technology which
is a single point radiation monitoring dosimeter. It is a useful
tool in measuring the patient dose, and it is an ideal solution in
multiple settings, including diagnostic radiology, nuclear
medicine, interventional procedures and radiation oncology
(LANDAUER).
Nanodots dosimeters also have minimal angular or energy
dependencies with appropriate calibration which can be used to
measure skin dose at a point of interest. Moreover, LANDAUER
provides a set of calibration dosimeters exposed at a beam
quality of 80 kVp on a PMMA phantom at normal incidence for
conventional (non-mammography) diagnostic radiology
applications. For radiation oncology applications, LANDAUER
provides a set of screened, unexposed calibration dosimeters
that can be irradiated using a radiation therapy beam quality.
Another way for calibration is to request a dosimeter set
exposed to a 662 keV beam quality (Cs-137).
The Nanodot dosimeters were placed on three different
locations (Anterior-Posterior, Lateral (LAT) and Posterior-
Anterior) as shown in figure 1, and the dose to the skin was
measured at these locations.
1

3
CT TABLE
2
Figure1: The phantoms in the middle of the CT scan and 1 is the
AP location, 2 is the LAT location and 3 is the PA location.
Experimental set-up and procedure:
The CTDIvol displayed by the scanner was validated to the true
CTDIvol following the ACR testing guidelines. A correction
factor was used to correct the inaccuracies in the displayed
value. This correction was applied to the DLP displayed by the
scanner.
Peak skin dose and its relation were measured by the 2
phantoms, and the phantoms were aligned at the isocenter of the
scanner and a single axial CT scan was made. After placing the
Nanodot dosimeters on the AP, LAT and PA locations, the
phantoms were scanned over the scan length for a fixed value of
the tube current. The measurement was repeated several times
using various scanning techniques (with varying energy,
current) as shown in table 1. Size conversion factors used were
based on the dimension of the phantom being scanned. These K-
factors with the CTDIvol produced the size-specific dose
estimates (SSDEs), and since the CT dose index was provided at

the CT scanner too, the size-specific dose estimate for the
phantoms was calculated. Also testing if the correlation between
the size-specific dose estimate and the measurement of the peak
skin dose match was done, and since such a relationship exists,
finding that factor was achieved.
Results:
After measuring the Peak Skin Dose and Size Specific Dose
Estimates (SSDE), a comparison was done. The SSDE was
calculated using the corresponding k-factor based on the AP and
lateral dimension from TG204 and the CTDIvol value which
was displayed on the console (SSDE = CTDIvol x K factor).
The conversion factor based on the use of the 32 cm diameter
NEMA phantom for CTDIvol was 1.35 for the AP and PA
locations, and the conversion factor for the Lat location was
1.55. Also, the AAPM Report 204 stated that the conversion
factor based on the use of the 16-cm diameter ACR phantom
was 0.89 for the three locations.
Figure 1: The graph illustrates the relationship between Peak
Skin Dose in AP location and the Size Specific Dose Estimates
in AP location in 32 cm NEMA phantom and 16 cm ACR
phantom.
The figure above illustrates the measured PSD in AP location
against the SSDE in AP location with using 2 different
phantoms (32-cm NEMA phantom and 16-cm ACR phantom).
For both phantoms, there was linear relationship between the
size specific dose estimates and the peak skin dose. In this
study an R-squared value was used to value the data in the
graphs and to tell how accurate the line is. In this study, the R-
squared value was 0.21 which indicate that 21% of the variance
of the dependent variable being studied is explained by the

variance of the independent variable. Therefore, the relationship
between the PSD in AP location and the SSDE in AP location
has a weak correlation.
Figure 2: The graph demonstrates the relationship between Peak
Skin Dose in PA location and the Size Specific Dose Estimates
in PA location in 32 cm NEMA phantom and 16 cm ACR
phantom.
The second figure demonstrates the measured PSD in PA
location against the SSDE in PA location. For both phantoms,
there was linear relationship between the size specific dose
estimates and the peak skin dose. In this graph, the R-squared
value was 0.66. Therefore, the relationship between the PSD in
PA location and the SSDE has a moderate positive relationship,
so a correlation might occur.
Figure 3: The graph illustrates the relationship between Peak
Skin Dose in Lateral location and the Size Specific Dose
Estimates in Lateral location in 32 cm NEMA phantom and 20
cm ACR phantom.
The third figure illustrates the measured PSD in the lateral
location against the SSDE in Lateral location. For both
phantoms, there was linear relationship between the size
specific dose estimates and the peak skin dose. In this graph the
R-squared value was 0.61 which indicated that there was a
moderate positive relationship between the PSD in lateral
location and the SSDE in lateral location.
In all the plots, linear relationship between the PSD and SSDE
was found, and the linear fitting equation was calculated by
Excel. (SSDE = 3.4827 x (PSD) + 5.522), this was the fitting
equation for the AP location graph (1st graph). However, since

there was a weak correlation between the PSD and SSDE in the
AP location, calculating the SSDE will not be accurate.
(SSDE = 6.7198 x (PSD) + 2.1234) and (SSDE = 8.2489 x
(PSD) + 2.3624), Those two linear fitting equations were for the
PA location graph (2nd graph) and lateral location graph (3rd
graph) respectively. Both equations have a moderate positive
relationship. Therefore, predicting the value of SSDE or PSD
will be possible but not 100% accurate. With using these data
and fitting equations, a physicist can estimate the PSD, but with
some limitations. The physicist would be within 30% the true
dose estimates and a large error would be there as well. The
regression was almost 65% in both locations, so roughly 65% of
the data points will fall close to the linear line.
Other trend line equations such as exponential, logarithmic,
polynomial and power were tested to evaluate the measured
PSD and SSDE, but the linear fitting equation was the only one
that the line fitted with the data.
Discussion:
The anterior Peak Skin Dose was different in the AP and LAT
locations comparison with the lateral location which is because
the thickness of the phantom. Considering that examination is
performed in the lateral location of the body which has the
highest x-ray attenuation, thus requiring higher beam energy to
penetrate. With increasing the patient average diameter, the
peak skin dose was higher. According to the data that was
measured, the measured PSD was higher in all the lateral
location than the AP and PA locations. The bigger the phantom
(more tissue to penetrate), the more dose was required to
attenuate and reached the dosimeter.
In the is study the AP and lateral dimensions of the phantom

were used to measure the SSDE which is a factor that is used to
estimate the absorbed dose. This could’ve been an error in
measuring the peak skin dose since the SSDE was not measured
at that time. Also, there was a linear relationship between the
PSD and the SSDE because the Size Specific Dose Estimates
dictate the patient’s dose and this could be one of the reasons
that the linear relationship occurred. Also, there could be better
modifications to the K-factors in order to dictate the patient’s
more accurately.
When calculating how much radiation dose a patient is actually
receiving, it’s best to consider their actual size. CTDIvol and
DLP are common methods to estimate a patient's radiation dose
from a CT procedure. The dose is the same regardless of patient
size, but the size of the patients is a factor in the overall
patient's absorbed dose. Therefore, SSDE measured in mGy,
would allow the physicists to use the patient’s size as a factor in
order to estimate the radiation dose. In the other hand the PSD
is the maximum absorbed dose in mGy to the most heavily
exposed region of the skin in specific location. In this study, the
measured values of the PSD and SSDE had a linear relationship
in most projections (C-spine, thoracic and pelvis). The higher
the PSD was, the higher the SSDE which was due to the
measured CTDIvol which displayed in the console (the higher
the CTDIvol was, the higher SSDE was calculated).
There is different between the CTDIvol that was shown on the
console and the actual CTDIvol. The CTDIvol or its derivative
the DLP, as seen on consoles and outputted, do not represent the
actual absorbed or effective dose for the patient. They should be
taken as an index of radiation output by the system for
comparison purposes. In this study, it is not possible to compare
the true CTDIvol to the displayed because the phantoms that
were used were not CTDI phantoms, so it is not possible to
place a CTDI probe.
However, nowadays many modifications to original CTDI

concept have attempted to make it more accurate patient
dosimetry method, with mixed results. Body CTDIvol reported
by the CT scanner, or measured on a CT scanner, is a dose
index that results from air kerma measurements at two
locations, to a very cylinder of plastic phantom with a density
of 1.19 g/cm3 (Morgan, M. 2021).
According to the measured data, some scan projections such as
abdomen had high PSD and high SSDE due to the high
measured CTDIvol and DLP caused out wire and low
regression. Taking out the abdomen PSD and SSDE from the
graphs make the regression higher (more positive) which means
correlation could exist. Therefore, some projections such as an
abdomen and head might make the data points and graphs not
clear and hard to be read.
When graphing the measured PSD and SSDE in each phantom
separately, a higher regression (more positive correlation) was
found (close to 90%) in all the three locations. This means that
the closer the patient to become cylindrical, the better
relationship between PSD and SSDE will be and more accurate
doses will be measured. It fails at very large effective
circumferences with perfectly cylindrical patients.
Conclusion:
The results showed there is a moderate positive relationship in
both PA and lateral locations, so there might be a correlation
between the PSD and SSDE. There is some promises in
Posterior and Lateral angles because the higher the PSD was,
the higher the SSDE was in most projections. The measured
PSD and SSDE showed that a physicist can estimate the PSD
within 30% the true dose estimates with a large error due to the
moderate positive relationship.
Further studies with more data should be done to prove or
decline the hypothesis. In this study, only two phantoms were
used (NEMA and ACR phantoms) with 32 cm and 16 cm
thicknesses, so other phantoms such as anthropomorphic

phantoms and fake human phantoms with different thickness
styles could be used to get better data and correlation.
In this study, only 8 measurements were taken in the three
different location due to the limitation of the Nanodats. More
measurements could have been taken and a better data points
would have been measured. With more date testing if the SSDE
has a sufficiently strong linear relationship with PSD could be
done.
Reference:
Jones, A. K., Kisiel, M. E., Rong, X. J., & Tam, A. L. (2021).
Validation of a method for estimating peak skin dose from
CT‐ guided procedures. Journal of applied clinical medical
physics.
Pontone, G., Scafuri, S., Mancini, M. E., Agalbato, C.,
Guglielmo, M., Baggiano, A., ... & Rossi, A (2021). Role
of computed tomography in COVID-19. Journal of
cardiovascular computed tomography, 15(1), 27-36.
De las Heras, H., Minniti, R., Wilson, S., Mitchell, C., Skopec,
M., Brunner, C. C., & Chakrabarti, K. (2013). Experimental
estimates of peak skin dose and its relationship to the CT dose
index using the CTDI head phantom. Radiation protection
dosimetry, 157(4), 536-542.
Tack, D., & Gevenois, P. A. (2018). Radiation dose from adult
and pediatric Multidetector computed tomography. Springer
Science & Business Media.

Coy, D., Kanne & Lin, E. (2018). Body CT the essentials.
McGraw-Hill Education / Medical.
Moniruzzaman, M., & Hossain, A. (2018). Pediatric and adult
body CT examinations: Size-specific effective dose estimates in
pediatric and adult body CT examinations for Polymethyl
Methacrylate phantom. LAP Lambert Academic Publishing.
Zhang, D. et al.Peak skin and eye lens radiation dose
from brain perfusion CT based on Monte Carlo simula-
tion. AJR 198, 412–417 (2012).
tion. AJR 198, 412–417 (2012).
tion. AJR 198, 412–417 (2012).
Zhang, D. et al. Peak skin and eye lens radiation dose from
brain perfusion CT based on Monte Carlo simulation. AJR
198, 412–417 (2012).
McCollough, C. H., Leng, S., Yu, L., Cody, D. D., Boone, J. M.
and McNitt-Gray, M. F. CT dose index and patient dose: they
are not the same thing. Radiology 259(2), 311 – 316 (2011).
Bauhs, J. A., Vrieze, T. J., Primak, A. N., Bruesewitz, M. R.
and McCollough, C. H. CT dosimetry: Comparison of
measurement techniques and devices. Radiographics 28, 245 –
253 (2008).
Beganovic, A., Sefic-Pasic, I., Skopljak-Beganovic, A., Kristic,
S., Sunjic, S., Mekic, A., Gazdic-Santic, M., Drljevic, A. and
Samek, D. Doses to skin during dynamic perfusion computed
tomography of the liver. Radiat. Prot. Dosim. 153(1), 106–111
(2013).
Publications. AAPM Publications - AAPM Reports. (n.d.).
Retrieved November 19, 2021, from
https://www.aapm.org/pubs/reports/.

LANDAUER, 50749 Nano-Dot™ Dosimeter Patient Monitoring
Solution
s
NCRP Report 116, Limitation of Exposure to Ionizing
Radiation, National Council on Radiation Protection
and Measurements, Bethesda, MD, 1993
Center for Devices and Radiological Health. (n.d.). Radiation
Dose Quality Assurance: Questions and Answers. U.S. Food and
Drug Administration. Retrieved November 19, 2021, from
https://www.fda.gov/radiation-emitting-products/initiative-
reduce-unnecessary-radiation-exposure-medical-
imaging/radiation-dose-quality-assurance-questions-and-
answers.
NanoDot™. LANDAUER. (n.d.). Retrieved November 19, 2021,
from https://www.landauer.com/product/nanodot.
ACR–sar–SPR practice parameter for the performance of ...
(n.d.). Retrieved November 19, 2021, from
https://www.acr.org/-/media/ACR/Files/Practice-
Parameters/CT-Entero.pdf.

Frost, J. (2021). How To Interpret R-squared in Regression
Analysis. Retrieved 1 December 2021, from
https://statisticsbyjim.com/regression/interpret-r-squared-
regression/
Morgan, M. (2021). Size specific dose estimate | Radiology
Reference Article | Radiopaedia.org. Retrieved 1 December
2021, from https://radiopaedia.org/articles/size-specific-dose-
estimate?lang=us
1.9E-
20.550000000000000040.596999999999999980.9639999999999
99971.083.00499999999999990.616999999999999992.7160000
00000000213.581.833.398.619999999999999213.6136.9799999
999999973.3516.3
Peak Skin Dose (mGy)
Size Specific Dose Estimates (mGy)
1.2999999999999999E-
20.6210.750.951999999999999960.987999999999999992.73300
000000000012.78299999999999993.685999999999999911.741.

833.397.4511.7731.973.3516.3
3.9E-
20.487999999999999990.711999999999999971.1691.25499999
999999993.22699999999999990.593999999999999973.0539999
99999999811.741.833.397.4511.7731.973.3516.3
1
2

Sheet1NUR-621 Topic 8: Staffing
MatrixCensus30292827262524Direct CaregiversScheduled
Hours Shift LengthNumber of StaffDay ShiftRN36 hrs12
hrs7777766NA36 hrs12 hrs3333322Health Unit Coordinator
36 hrs12hrs1111111Night ShiftRN36 hrs12 hrs7777766NA36
hrs12 hrs3333322Health Unit Coordinator36 hrs12 hrs1111111
Scenario: You are the nurse leader of a 30-bed medical surgical
unit and have to account for all staffing, including any
discrepancies. Using sound financial management principles,
complete the “Staffing Matrix" (COMPLETED AND
ATTACHED)
After completing the matrix, compose an 1,000-1,250-word
reflection answering the following questions:
1. Why is it important to use a staffing matrix in your heal th
care setting?
2. Briefly describe your staffing matrix. How many FTEs (full -
time equivalent) on the staffing roster are required to cover
daily needs? What units of services or work measurement did
you use and why? What financial management principles did
you use to determine your staffing matrix?

3. Explain how you adjusted your staffing based on changes in
the patient census.
4. You receive your financial report for the month. You have
used more FTEs than what was budgeted for your census. How
will you make up the variance? How would you reallocate
resources to make up for the variance and still comply with
guidelines?
· Include two to four peer-reviewed references in your essay,
including the textbook: Penner, S.J., 2016. Economics and
financial management for nurses and nurse leaders. Springer
Publishing Company.
Prepare according to the guidelines found in the APA Style
Guide 7th edition.
This assignment uses a rubric (Below). Please review the rubric
prior to beginning the assignment to become familiar with the
expectations.
Benchmark - Staffing Matrix and Reflection - Rubric
10 points
Criteria Description

5. Excellent
10 points
The reflection substantially describes why it is important to use
a staffing matrix in a health care setting.
4. Good
9.2 points
The reflection clearly describes why it is important to use a
staffing matrix in a health care setting.
3. Satisfactory
8.8 points
The reflection provides a basic description of w hy it is
important to use a staffing matrix in a health care setting.
2. Less Than Satisfactory
8 points
The reflection does not sufficiently describe why it is important
to use a staffing matrix in a health care setting.
1. Unsatisfactory
0 points
The reflection provides an incomplete description of why it is
important to use a staffing matrix in a health care setting.
Staffing Matrix Description (C6.4)
10 points
Staffing Matrix Description (C6.4)

5. Excellent
10 points
The reflection comprehensively describes the staffing matrix,
how many FTEs should be on the staffing roster to cover daily
needs, the units of services or work measurement that were
used, and the financial management principles that were used to
determine the staffing matrix.
4. Good
9.2 points
The reflection thoroughly describes the staffing matrix, how
many FTEs should be on the staffing roster to cover daily
needs, the units of services or work measurement that were
used, and the financial management principles that w ere used to
determine the staffing matrix.
3. Satisfactory
8.8 points
The reflection clearly describes the staffing matrix, how many
FTEs should be on the staffing roster to cover daily needs, the
units of services or work measurement that were used, and the
financial management principles that were used to determine the
staffing matrix.
8 points
The reflection vaguely describes the staffing matrix, how many

staffing matrix.
1. Unsatisfactory
0 points
The reflection does not adequately describe the staffing matrix,
staffing matrix.
Staffing Adjustments
10 points
Staffing Adjustments
5. Excellent
10 points
The reflection thoroughly explains what adjustments were made
to the staffing based on changes in the patient census.
4. Good
9.2 points
The reflection comprehensively explains what adjustments were
made to the staffing based on changes in the patient census.
3. Satisfactory
8.8 points
The reflection clearly explains what adjustments were made to

the staffing based on changes in the patient census.
8 points
The reflection vaguely explains what adjustments were made to
the staffing based on changes in the patient census.
1. Unsatisfactory
0 points
The reflection does not adequately explain what adjustments
were made to the staffing based on changes in the patient
census.
Variance
10 points
Variance
5. Excellent
10 points
The essay includes a credible description about what will help
make up the variance along with how to reallocate resources to
make up for the variance while complying with guidelines.
4. Good
9.2 points
The essay includes a creative and realistic description about
what will make up the variance, including how to reallocate
resources to make up for the variance while complying with
guidelines.

3. Satisfactory
8.8 points
The essay includes a realistic description about what will help
make up the variance along with how to reallocate resources to
8 points
The essay includes a vague description about what will help
make up the variance or how to reallocate resources to make up
for the variance while complying with guidelines.
1. Unsatisfactory
0 points
The essay does not include a sufficient description about what
will help make up the variance or how to reallocate resources to
Matrix
30 points
Matrix
5. Excellent
30 points
The matrix is completed with appropriate staffing numbers.
4. Good
27.6 points
Not Applicable

3. Satisfactory
26.4 points
The matrix is completed with mostly appropriate staffing
numbers.
24 points
Not Applicable
1. Unsatisfactory
0 points
The matrix is not completed with appropriate staffing numbers
Thesis Development and Purpose
7 points
Thesis Development and Purpose
5. Excellent
7 points
Thesis is comprehensive and contains the essence of the paper.
Thesis statement makes the purpose of the paper clear.
4. Good
6.44 points
Thesis is clear and forecasts the development of the paper.
Thesis is descriptive and reflective of the arguments and
appropriate to the purpose.
3. Satisfactory
6.16 points

Thesis is apparent and appropriate to purpose.
5.6 points
Thesis is insufficiently developed or vague. Purpose is not
clear.
1. Unsatisfactory
0 points
Paper lacks any discernible overall purpose or organizing claim.
Argument Logic and Construction
8 points
Argument Logic and Construction
5. Excellent
8 points
Clear and convincing argument that presents a persuasive claim
in a distinctive and compelling manner. All sources are
authoritative.
4. Good
7.36 points
Argument shows logical progressions. Techniques of
argumentation are evident. There is a smooth progression of
claims from introduction to conclusion. Most sources are
authoritative.
3. Satisfactory
7.04 points

Sufficient justification of claims is lacking. Argument lacks
consistent unity. There are obvious flaws in the logic. Some
sources have questionable credibility.
6.4 points
Argument is orderly, but may have a few inconsistencies. The
argument presents minimal justification of claims. Argument
logically, but not thoroughly, supports the purpose. Sources
used are credible. Introduction and conclusion bracket the
thesis.
1. Unsatisfactory
0 points
Statement of purpose is not justified by the conclusion. The
conclusion does not support the claim made. Argument is
incoherent and uses noncredible sources.
Mechanics of Writing (includes spelling, punctuation, grammar,
language use)
5 points
Mechanics of Writing (includes spelling, punctuation, grammar,
language use)
5. Excellent
5 points
Writer is clearly in command of standard, written, academic
English.

4. Good
4.6 points
Prose is largely free of mechanical errors, although a few may
be present. The writer uses a variety of effective sentence
structures and figures of speech.
3. Satisfactory
4.4 points
Some mechanical errors or typos are present, but they are not
overly distracting to the reader. Correct and varied sentence
structure and audience-appropriate language are employed.
4 points
Frequent and repetitive mechanical errors distract the reader.
Inconsistencies in language choice (register) or word choice are
present. Sentence structure is correct but not varied.
1. Unsatisfactory
0 points
Surface errors are pervasive enough that they impede
communication of meaning. Inappropriate word choice or
sentence construction is used.
Paper Format (Use of appropriate style for the major and
assignment)
5 points
Paper Format (Use of appropriate style for the major and

assignment)
5. Excellent
5 points
All format elements are correct.
4. Good
4.6 points
Template is fully used; There are virtually no errors in
formatting style.
3. Satisfactory
4.4 points
Template is used, and formatting is correct, although some
minor errors may be present.
4 points
Template is used, but some elements are missing or mistaken;
lack of control with formatting is apparent.
1. Unsatisfactory
0 points
Template is not used appropriately or documentation format is
rarely followed correctly.
Documentation of Sources
5 points
Documentation of Sources (citations, footnotes, references,
bibliography, etc., as appropriate to assignment and style)

5. Excellent
5 points
Sources are completely and correctly documented, as
appropriate to assignment and style, and format is free of error.
4. Good
4.6 points
Sources are documented, as appropriate to assignment and style,
and format is mostly correct.
3. Satisfactory
4.4 points
Sources are documented, as appropriate to assignment and style,
although some formatting errors may be present.
4 points
Sources are not documented.
1. Unsatisfactory
0 points
Documentation of sources is inconsistent or incorrect, as
appropriate to assignment and style, with numerous formatting
errors.
Total 100 points

EXAMPLE Benchmark – Staffing Matrix and ReflectionPerformanc

EXAMPLE Benchmark – Staffing Matrix and ReflectionPerformanc

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