1
www.FLCenterForNursing.org
Addressing Nurse Workforce Issues for the Health of Florida
2018
Technical Documentation
for Licensure and
Workforce Survey
Data Analysis
2
Contents
Background ..................................................................................................................................... 3
Data Extract .................................................................................................................................... 3
Data Cleaning.................................................................................................................................. 5
Nurse Placement in Counties and Regions ..................................................................................... 5
Identifying the Potential Nurse Workforce ..................................................................................... 6
Survey Response Rates and Bias Analysis ..................................................................................... 8
Estimation of the Actual Nurse Workforce .................................................................................... 9
Conclusions and Limitations of the Data Sources ........................................................................ 12
References ..................................................................................................................................... 13
Appendix A: 2016-2017 Workforce Survey Questions ................................................................ 14
Appendix B: County Composition of FCN Regions .................................................................... 17
April 2018 Technical Documentation for Licensure and Workforce Survey Data 3
Technical Documentation for Licensure and Workforce Survey Data Analysis
Background
The Florida Center for Nursing (Center) was established in 2001 to address issues related to
nurse supply, demand, and shortage in this state. The nurse licensure database maintained
by the Florida Board of Nursing (FBON) is one important source of information on the state’s
nurse supply. The licensure database contains the most complete information available in
the state specific to the regulation of nurses. Information includes the number of licensed
nurses, their eligibility to practice, demographic characteristics, and their distribution across
the state of Florida.
Licensure data collected by the FBON do not include information about the work behavior of
nurses. This limits their usefulness for strategic labor force planning. Data do not indicate
whether nurses are working (either in or outside the field of nursing), how much they work,
or in what setting. Because the Center is primarily interested in the amount of nursing labor
provided in Florida, in contrast to the number of Florida nursing licenses that are held,
licensure data are cleaned and subset to isolate nurses who could reasonably be practicing
nursing within the state of Florida. We call this subset the potential nurse workforce.
In 2008, the Center began working with FBON and Medical Quality Assurance (MQA) to
integrate a voluntary Workforce Survey into the online renewal process for nurses. The
Workforce Survey generates important data for workforce analysis, such as work status,
hours worked, and highest degree held. Using a unique file number as an identifier,
workforce survey data are merged with licensure data so that members of the potential
nurse workforce can be counted as actually working in nursing, if they indicate that they are.
A substantial majority of renewing nurses participate in the Workforce Survey each year, but
a number of cases still lack workforce data. In addition to those who do not complete the
voluntary survey during renewal, nurses newly licensed in Florida are not exposed to the
Workforce Survey until they renew their licenses for the first time.
1
The Center uses
information that we have about these non-respondents (e.g. practicing address and
demographic characteristics) to estimate employment status and comparisons to survey
participants, when possible. This document provides technical details about the process of
merging, cleaning, and estimating values for some cases using weighting and imputation
techniques based on licensure and Workforce Survey data.
Data Extract
In 2006, the Center and FBON reached an agreement whereby licensure data is regularly
provided to the Center as a data extract (a static file) drawn from the dynamically changing
licensure database. This extract includes records for each nursing license held in Florida by
Registered Nurses (RN), Advanced Registered Nurse Practitioners (ARNP), Clinical Nurse
1
In the 2016-2017 cohort, together these groups comprised approximately 29% of the potential nurse workforce.
Renewing nurses who did not participate in the survey comprise 16.7% of the potential workforce, and newly licensed,
clear and active nurses represent 12.6% of the potential workforce.
Technical Documentation
for Licensure and
Workforce Survey
Data Analysis
April 2010
April 2018 Technical Documentation for Licensure and Workforce Survey Data 4
Specialists (CNS), and Licensed Practical Nurses (LPN). Each record contains information on
license type (RN, ARNP, CNS, or LPN), license status (e.g., active, suspended), date of birth,
gender, race/ethnicity, year of initial licensure in Florida, application type (i.e. examination
or endorsement), and mailing and/or practicing addresses.
An extract of Workforce Survey data is provided to the Center as a separate file along with
the monthly licensure data extract. The questionnaire used is presented in Appendix A.
Because nurses renew biennially, it takes two years of renewals to amass a complete cohort’s
survey data from all renewing nurses choosing to take the survey. Renewal cycles are based
on the licensee’s expiration date of either April 30
th
or July 31
st
.
2
In even years (e.g. 2016),
approximately two-thirds of RNs and ARNPs are expected to renew between January and
July. In odd years (e.g. 2017), the remaining one-third of RNs and ARNPs renew from January
through April and all LPNs renew from March through July.
Data used for the current analyses intend to represent all renewals and new licenses
between January 1, 2016 and December 31, 2017. Data extracts are received by the Center
on the 21
st
of every month. For analyses of the 2016-2017 cohort, the Center compared and
combined December 2017 and January 2018 extracts to best represent the population of
licensees as of December 31
st
.
3
This cutoff represents the transition to the next biennial
renewal cohort, beginning on January 1 of the next even year (e.g. 2018).
The licensure data extract was merged with Workforce Survey data collected during 2016
and 2017 using an MQA generated file number as the unique identifier to join records.
Though surveys are completed throughout the two-year period, we treat survey data as
representing the best possible estimate of a nurse’s work status at the end of the renewal
cycle. Each record in the potential nurse workforce is assigned an estimated work status
(using their practicing address provided through the MQA renewal process when survey
responses are not available). A full-time equivalent (FTE) value is estimated for all survey
respondents and weighted to more closely represent the total population, including non-
respondents. Thus, the merged dataset contains the best possible estimate of the workforce
as of January 2018.
2
With the exception of Temporary Military Active licenses which expire exactly one year from the date received.
Nurses practicing in the state of Florida with non-expired temporary military licenses are included in this sample.
3
The December 21
st
data extract is the primary source of data. To avoid excluding any renewal records and survey
responses between December 21
st
and December 31
st
, data from the January extract are incorporated when a) no record
is found for that nurse in the December file or b) the nurse’s license or active status changed between December and
January. In both situations, only nurses whose records indicate a license expiration date consistent with the current
cohort of interest are included. For instance, 2016-2017 renewals should have an expiration date of 2018 or 2019. Any
nurses whose expiration date was after July 31, 2019 as of the January data extract was excluded from the sample and
will be analyzed with the next cohort. This approach provides the added benefit of capturing renewals and new licenses
through the end of the calendar year of the renewal cohort, allowing for inclusion of new licensees graduating in
December, and allows nurses who missed their renewal deadlines an additional five to seven months to complete
renewal for inclusion in the sample. This approach is new as of the 2018 reporting cycle and may affect response rates.
April 2018 Technical Documentation for Licensure and Workforce Survey Data 5
Data Cleaning
Licensure data are first cleaned for implausible dates of birth and initial licensure. The data
contain some records with clearly inaccurate birth years dating back to the 19
th
century. The
lower limit for age was placed at 18 and the upper limit is 97. Nurses whose birth years place
their age outside of this range were recoded as missing.
Dates of initial licensure are also inspected for implausible dates. In particular, some records
indicate initial license dates of January 1, 1901, a value which represents missing data in the
MQA licensure file. Frequencies of original licensure dates were explored to identify these,
and any other potential implausible dates to be recoded as missing. In the current data set,
the earliest plausible date of initial licensure was the year 1951, representing nurses that
have been continuously licensed for 66 years. An important note regarding this attribute is
that initial licensure dates may be “reset” if nurses allow their licenses to expire but later
become licensed again in the state. Since it is not possible to tell whether this has happened
to nurses in the licensure extract we receive, the measure must be interpreted with caution.
Average tenure as a nurse in Florida may be underestimated by these data.
Missing data generated by cleaning procedures are typically minimal. In the 2016-2017
cohort, only 21 of the 426,555 records in the December/January licensure data extract had
an original licensure date of January 1, 1901, and approximately 40 records indicated
implausible ages beyond the upper or lower limits. Naturally occurring missing data also
exist for other variables in the licensure database.
4
Approximately 1% of the records were
missing data on gender. Similarly, 1% of cases were missing data on race/ethnicity.
Nurse Placement in Counties and Regions
Stakeholders depend on local data for nurse workforce planning, so the Center provides as
much detail as possible at regional and county levels. We use both Workforce Survey data
and address information from the licensure database to identify nurses who report living
and/or working in the state of Florida.
5
Nurses with Florida addresses are placed into
counties, Regional Workforce Boards, and larger regions of the state consisting of multiple
workforce boards. There are 67 counties and 24 Regional Workforce Boards, a classification
used by Workforce Florida, Incorporated for workforce planning and outreach efforts. See
Appendix B for a map of Florida showing the regions into which each county falls.
The licensure database contains two sets of address fields used to place nurses into Florida
regions/counties: a mailing address (where nurses wish to receive mail from the FBON) and
4
In the past, the Center’s analyses of previous cohorts included cases with non-missing values for a given variable.
The current analysis attempts to include cases with missing values, when possible, to most accurately represent the
proportion of participants with a given characteristic. Inclusion of cases with missing values are notated in the report,
when applicable. This may impact comparisons of previous years.
5
In previous versions of the Workforce Survey, participants were asked to provide their practicing and/or home
counties. In these instances, survey data would supersede address data from the licensure file. However, efforts to
minimize respondents’ time spent on the Workforce Survey resulted in the elimination of this question. Thus, regional
and county information is based entirely on the practicing or mailing addresses provided at renewal. Since it is
unknown how recently the address fields in the licensure database have been updated, the accuracy of regional
placement should be interpreted with caution.
April 2018 Technical Documentation for Licensure and Workforce Survey Data 6
a practice address. When nurses provide valid practice location information, they are placed
according to the county in which they are employed. In all other cases, nurses are placed
according to the county in which their mailing address is located. Licensees with no valid
mailing or practicing address information are coded as missing in their regional placement,
but remain in the sample if they hold an active Florida license.
This procedure maximizes the accuracy of placement for describing workforce distribution
as much as can be expected despite data limitations. Address data do not allow a clean
analysis of where nurses live and work, although they do allow identification of nurses who
report working or receiving mail outside of Florida. Since mailing addresses may be a home
residence or a work location – wherever the nurse wishes to receive mail from the FBON –
precision is difficult to obtain for nurses who do not have valid and distinct addresses in both
fields. Since many nurses are likely to commute from one county to another in order to work,
county placement is probably least accurate for describing the distribution of the nursing
workforce. More accuracy is probable in larger geographic areas since the areas are more
likely to encompass both work and residence locations.
Additionally, many records contain incomplete address data and data entry errors, such as
zip code values that do not match their reported city and/or state. When possible, many of
these errors are manually fixed in the data cleaning process to place addresses into the
correct counties. Each licensee’s recorded zip code is compared against a SAS statistical
software lookup table of zip codes which matches the data extract as closely as possible.
6
In
the cases where given zip codes are invalid, additional address information provided is used
to lookup the most likely county match. The process typically creates a county placement for
several hundred Florida addresses which were previously unplaced and may have otherwise
been dropped from the sample as having no valid Florida address.
Identifying the Potential Nurse Workforce
A multi-step process is used to generate a subset from the total file representing the potential
nurse workforce: those eligible to work as nurses and providing a Florida address. First,
nurse records are dropped if their license and active status indicate that they do not maintain
a Florida license,
7
(e.g. retired, delinquent, suspended). Next, nurses who are living and/or
working out of state are dropped, including nurses who provided non-Florida mailing and
practice addresses (n = 50,837), as well as those who provided a Florida mailing address but
indicated an out of state practicing address (n = 2,464).
In addition to having a Florida mailing and/or practicing address, nurses’ licenses must be
clear and active in order to be included in the potential nurse workforce. Records indicating
any other status (e.g. inactive, conditional, obligations) are removed from the sample. Figure
1 details the number of licensees excluded in each step of this process for the 2016-2017
cohort (includes renewals and newly licensed nurses). The categories for removal may not
be mutually exclusive (e.g. someone without a Florida address may also have an inactive
6
Zip codes change over time but are reasonably steady in the short term. SAS makes new zip code lookup tables
available quarterly at http://support.sas.com/rnd/datavisualization/mapsonline/html/misc.html
7
Based on the Florida Department of Health, Medical Quality Assurance (2018) definitions of license statuses
April 2018 Technical Documentation for Licensure and Workforce Survey Data 7
license), therefore counts of nurses removed at each stage may not encompass the true
counts of every nurse with a given status.
Figure 1 2016-2017 Licensees Excluded from Analysis, by Reason
In addition to those excluded for not maintaining a valid Florida nursing license, residing
and/or working outside of Florida, or not maintaining a Clear/Active license status, newly
licensed nurses were also excluded from analysis of survey data. As mentioned above, newly
licensed nurses are not overtly exposed to the Workforce survey until they complete their
first renewal. As a result, characteristics of newly licensed nurses were explored separately
from those renewing their licenses.
8
Table 1 highlights these exclusions, in addition to those
previously described, by rank.
Table 1. 2016-2017 Exclusions and Potential Nurse Workforce, by Rank
All Florida
Licensees
Excluded
Newly
Licensed
Renewal
Workforce
319,598
78,043
32,559
208,996
29,642
5,222
834
23,586
77,131
17,261
7,698
52,172
184
36
7
141
426,555
100,562
41,098
284,895
In the 2016-2017 cohort, the average age of excluded nurses was 50.9. Approximately 69%
were white, and about 88% were female. The clear and active newly licensed nurses were
slightly more likely to be male (14.3%), substantially less likely to be white (48.1%), and
much younger than those excluded, with an average age of 35.6. These differences are
consistent with what would be expected of a younger, and increasingly diverse cohort of new
nurses. The population of clear and active nurses included in the renewal cohort for analysis
of the renewal cohort reflect similarities to those excluded from analysis. Overall, 88% of the
renewal cohort was female, 63% were white, and their average age is approximately 49.1.
8
Because some newly licensed nurses may have been excluded for license or address reasons in a prior step, the
number of newly licensed nurses excluded from the potential workforce for analysis purposes may not represent the
total number of new nurses licensed during this renewal cycle.
April 2018 Technical Documentation for Licensure and Workforce Survey Data 8
Survey Response Rates and Bias Analysis
The online Workforce Survey was available for completion between January 1, 2016 and
December 31, 2017. Nurses who renewed their licenses during this time were given the
option to complete this survey during the online renewal process.
9
Table 2 shows the response rates for the 2016-2017 Workforce Survey by renewal group
(the grouping based on the nurse’s expiration date)
10
and license type. Overall, a substantial
majority of renewing nurses (81%) participated in the survey
11
. The first and fourth renewal
groups had slightly higher response rates compared to groups two and three. Similarly,
ARNP/CNS nurses and LPNs participated in the survey at higher rates than the overall
sample. RNs had the lowest response rate (79%) compared to the other nurse types.
Table 2. 2016-2017 Nurse Workforce Survey Response Rates, by Renewal Bucket and
Rank
Renewal Bucket
Renewed
Completed
Survey
Response
Rate (%)
Group 1 (exp. April 2018)
50,298
42,204
83.9
Group 2 (exp. July 2018)
79,773
64,344
80.7
Group 3 (exp. April 2019)
102,652
78,762
76.7
Group 4 (exp. July 2019)
52,172
45,157
86.6
License Type
RN
208,996
164,460
78.7
ARNP/CNS
23,727
20,760
87.5
LPN
52,172
45,157
86.6
Total
284,895
230,377
80.9
These substantially high response rates increase the generalizability of the survey responses
to the overall population of renewing nurses. However, to examine potential patterns of bias,
we compared characteristics of survey respondents to those that did not participate in the
survey (Table 3, next page).
Nurses who participated in the survey are more likely to be white, compared to non-
participants, and a slightly higher proportion of non-participants are male. Survey
participants were about seven years older, on average, than those that did not take the
survey. Participants have also held a Florida nursing license for approximately twice as long
as non-participants, on average. Differences between average age and years licensed in
Florida were statistically significant between groups (p < 0.001), although significance may
9
In 2008, the Florida Board of Nursing began mailing postcard reminders for license renewal, instead of paper renewal
forms. The vast majority of nurses have been renewing their licenses online since this time, as paper renewal forms
must now be specially requested.
10
For example, nurses whose license expires on April 30, 2018 would have previously expired April 30, 2016. It is
expected that these nurses would have renewed between January and April of 2016.
11
It is important to note that this response rate is somewhat lower than the response rates indicated in previous cohorts.
This is more likely attributed to changes in the data cleaning procedure which kept more renewing nurses in the
sample, rather than an indication of a decreased participation rate.
April 2018 Technical Documentation for Licensure and Workforce Survey Data 9
be attributed (in part) to large sample sizes. These differences indicate that our survey data
may underrepresent younger and more diverse nurses, and should highlight the importance
of increasing response rates among these groups over time.
Table 3. 2016-2017 Demographic Comparison of Respondents and Non-Participants
Survey
Respondents
Non-
Participants
Race/Ethnicity
%
%
White
64.5
57.4
Black
16.0
17.6
Hispanic
10.8
15.2
Asian
5.7
4.9
Other
1.9
2.7
Gender
Female
89.2
86.5
Male
10.8
13.0
Age Group
21-30
6.0
21.3
31-40
19.2
26.2
41-50
23.7
20.5
51-60
26.7
17.4
61-70
20.4
11.0
71 or older
4.0
3.6
Average Age
50.4
43.8
Years Licensed in FL
16.3
8.5
Note: Proportions may not equal 100% due to missing data
Estimation of the Actual Nurse Workforce
Despite high participation rates, non-response does occur due to the voluntary nature of this
survey. In order to improve the representativeness of the FTE for all renewing nurses,
regardless of survey participation, we developed weights based on characteristics that were
available (or estimated) for the entire potential nurse workforce (n = 284,895).
12
Weighting
was incorporated into licensure and workforce survey for the first time in the 2016-2017
cohort. Some of the other states that use weighting in healthcare workforce survey analyses
include Virginia (n.d.), Utah (Harris & Ruttinger, 2017), and California (Spetz, Chu, Jura, &
Miller, 2017).
The first step was to determine employment status of all members of the potential workforce
sample (working or not working). For survey participants, this was primarily determined by
12
Because we have access to licensure data for the entire population of renewing nurses, and since all nurses have an
equal probability of participating in the survey, weights were only created to account for non-response, rather than
sampling bias (although accessibility and mobile friendliness of the survey should be explored in the future).
April 2018 Technical Documentation for Licensure and Workforce Survey Data 10
survey responses indicating employment, and supplemented by the state/zip code of their
practicing address or hours reported when employment status was missing. For renewing
nurses who did not respond to the survey, employment status was estimated exclusively by
determining if they had a valid practicing address listed. While this is an imperfect method,
it provides our best estimate of work status for nurses who do not participate in the survey.
In the 2016-2017 cohort, 88.6% of the renewing nurses, regardless of survey participation,
indicated or appeared to be employed at the time of renewal. Approximately 87.5% of the
survey participants were working, while approximately 93% of non-responders were
working. This indicates that working nurses are likely underestimated among survey
respondents.
13
Weight = Population Proportion
Sample Proportion
When applicable, weighted frequencies were applied to survey data to improve the
likelihood that the sample is representative of the population of renewing nurses, by
adjusting for non-response. Weights were calculated for each working status by age group
and rank. The following is an example of the weights, for renewing RN nurses:
Table 4. 2016-2017 Weights by Employment Status and Age Group, RN Nurses
Employed by Age
Population
Proportion (%)
Sample
Proportion (%)
Weight
Working
18-30 years old
8.85
5.29
1.67
31-40 years old
18.76
16.93
1.11
41-50 years old
20.71
21.13
0.98
51-60 years old
22.56
24.48
0.92
61-70 years old
15.39
17.24
0.89
71 or older
2.42
2.37
1.02
Not working
18-30 years old
0.27
0.21
1.29
31-40 years old
1.08
1.09
0.99
41-50 years old
1.56
1.71
0.91
51-60 years old
2.56
2.88
0.89
61-70 years old
3.93
4.49
0.88
71 or older
1.91
2.19
0.87
13
Analyses of previous cohorts involved random selection and placement of non-responders into dichotomous
employment grouping based on calculated proportions of working survey participants using a matrix of age by gender
by license type (Florida Center for Nursing, 2010). Determining work status by address is a simple alternative to
estimate work status, as mean imputation is better suited for item non-response rather than unit non-response (i.e.
survey non-participants). This should also reduce the likelihood that individual cases are inaccurately placed although
proportions should be interpreted with caution since renewing nurses may have outdated addresses.
April 2018 Technical Documentation for Licensure and Workforce Survey Data 11
We also used the estimated weights to identify the proportion of nurses with a given full-
time equivalency (FTE) employment status.
14
Survey respondents provided information on
the number of hours they worked, and this information was used to assign an FTE value with
the following formula:
FTE = (hours/week × weeks/year)
1,872
In this formula, the numerator represents the hours worked per year by the respondent, and
the denominator represents the minimum hours worked in a year if a nurse represents 1.0
FTE. The typical number of hours per year used in computations like this is 2,080, which is
based on 40 hours worked per week for 52 weeks. However, nurses who work 36 hours a
week (i.e. three 12-hour shifts) are considered full time employees, therefore the minimum
hours per year for 1.0 FTE status represents 36 hours per week for 52 weeks (1,872 hours).
Nurses working more than 1,872 hours per year were capped at 1.0 FTE, while those
working fewer than 1,872 hours per year were assigned an FTE fractional value. When a
nurse reported he or she was not working in nursing, their FTE was assigned a value of 0.
Some additional data cleaning was required. For instance, some participants described their
work status as full time, but indicated they worked 8 hours per week. When possible, records
were reviewed on an individual basis to identify if any clearer estimates could be determined
based on other responses.
Finally, using SAS, the weighted hot deck imputation technique was used
15
to estimate
employment type (full time, part time, or per diem) and hours worked per year (0 – 4160)
for working survey respondents with missing data. Among the 201,655 working survey
respondents, 1.3% of cases received imputed values for employment type and 1.8% received
imputed values for hours worked per year.
Although the FTE value for members of the potential nurse workforce is unknown if they did
not complete a Workforce Survey, the use of weighted proportions and imputed values
among survey respondents increased the ability for the sample data to represent the
estimated proportions of working and FTE status among the total population. In the 2016-
2017 cohort, this resulted in an estimated 158,608 working nurses with 1.0 FTE, and an
overall average weighted FTE of .92 for all working nurses.
14
The use of weights to estimate FTE for all renewing nurses is a vastly different approach from previous years’
analyses which involved a series of mean substitutions and a reliance on categorical survey data recoded to the
midpoints to represent hours worked. This shift in methodology may affect comparisons to previous years. For a full
explanation of the methodology used in the past, see the Florida Center for Nursing’s (2010) Technical Document.
15
Weighted hot deck imputation was considered a useful effort to minimize missing at random non-response among
survey respondents without using mean substitution. While this was not the initial preferred imputation technique, it
was selected based on limited computer processing power needed for more advanced technique. Similarly, more
advance techniques are mostly required for regression analyses not used in this research.
April 2018 Technical Documentation for Licensure and Workforce Survey Data 12
Conclusions and Limitations of the Data Sources
All analyses of the Florida nurse supply based on licensure and Workforce Survey data
inevitably suffer from some degree of missing or inaccurate data. The Center’s process for
cleaning the data, assigning nurses into counties and regions, and imputing missing data
attempts to correct some of the data problems which, if left unchecked, would distort our
view of the nurse supply. The exclusion process we use to identify the potential nurse
workforce generates our best estimate of nurses who could be working in Florida, including
their location in a specific region of Florida. However, it is important to reiterate that
licensure data do not indicate whether nurses are working in the field of nursing or how
much they work. If clean Workforce Survey data are available for a nurse, it is a
straightforward process to determine whether and where a nurse practices nursing.
However, due to the voluntary nature of this survey, population parameters are estimated
using imputation and weighting techniques. While these efforts improve the survey data by
providing statistical adjustments for non-response, the information for individual cases may
not be accurate. The only way to provide completely accurate information regarding the total
nurse supply in each cohort is to implement additional mandatory fields in the license
renewal process. Without this, assumptions about the population must be gleaned from
estimates among self-selected survey participants and address fields in the licensure
database, the latter of which are known to have problems.
The incorporation of the Workforce Survey beginning in 2008 has dramatically improved
data quality and facilitated our efforts to accurately quantify the nursing workforce. While
there will always be at least some missing data, possessing complete workforce information
on a substantial majority of renewing nurses is a huge benefit for nurse workforce analysis
and planning in Florida. We are appreciative of our continuing collaboration with the Florida
Board of Nursing and Medical Quality Assurance during each renewal cycle and will continue
to do our part to improve the quality of the Workforce survey, while making efforts to retain
the ability to provide trend analysis and maintain the National Forum of State Nursing
Workforce Centers’ National Nursing Workforce Minimum Dataset (2016) for nurse supply.
April 2018 Technical Documentation for Licensure and Workforce Survey Data 13
References
Florida Center for Nursing. (2010). Technical Documentation for Licensure and Workforce
Survey Data Analysis. Retrieved from
https://www.flcenterfornursing.org/DesktopModules/Bring2mind/DMX/Download.aspx?
Command=Core_Download&EntryId=384&PortalId=0&TabId=151
Florida Department of Health, Division of Medical Quality Assurance. (2018). License Status
Definitions. Retrieved from
https://appsmqa.doh.state.fl.us/MQASearchServices/LicStatus.html
Harris, A., & Ruttinger, C. (2017). Utah's Advance Practice Registered Nurse Workforce, 2017:
A Study on the Supply and Distribution of APRNs in Utah. The Utah Medical Education
Council. Retrieved from https://nursing.utahmec.org/wp-
content/uploads/APRN_2017_FINAL.pdf
Healthcare Workforce Data Center (HWDC). (n.d.). HWDC Methodology. Virginia Department
of Health Professions. Retrieved from
https://www.dhp.virginia.gov/hwdc/docs/MethodologyandGlossary.pdf
Spetz, J., Chu, L., Jura, M., & Miller, J. (2017). California Board of Registered Nursing 2016
Survey of Registered Nurses. Retrieved from
http://www.rn.ca.gov/pdfs/forms/survey2016.pdf
The National Forum of State Nursing Workforce Centers. (2016). Minimum Nurse Supply
Dataset. Retrieved from http://nursingworkforcecenters.org/wp-
content/uploads/2016/11/National-Forum-Supply-Minimum-Dataset_September-
2016.pdf
April 2018 Technical Documentation for Licensure and Workforce Survey Data 14
Appendix A: 2016-2017 Workforce Survey Questions
Q01: Year of Initial U.S. Licensure:
YYYY
Q02: In what country were you initially licensed as an RN or LPN?
Select Country
Q03: What type of nursing degree/credential qualified you for your first U.S. nursing license?
Vocational/Practical Certificate –
Nursing
Diploma – Nursing
Associate Degree – Nursing
Baccalaureate Degree – Nursing
Master’s Degree – Nursing
Doctoral Degree – Nursing
Q04: What is your highest level of education in NURSING?
Vocational/Practical Nursing Certificate
Diploma in Nursing
Associate Degree in Nursing
Baccalaureate Degree in Nursing
Master’s Degree in Nursing
PhD in Nursing
Doctorate of Nursing Practice
Other Nursing Doctoral Degree
Q05: What is your highest NON-NURSING degree?
Associate Degree – Non-Nursing
Baccalaureate Degree – Non- Nursing
Master’s Degree – Business Related
Master’s Degree – Health Related
Master’s Degree – Other
Law Degree (JD)
Doctorate in Medicine (MD, DO)
Doctoral Degree – Other Health
Discipline
Doctoral Degree – Other Discipline
No Degree Outside Of Nursing
Q06: Are you credentialed to practice as one of the following Advanced Practice Nurse
certifications?
Yes - Certified Registered Nurse Anesthetist
Yes - Certified Nurse Midwife
Yes - Nurse Practitioner (Any Specialties)
No
Q07: Do you perform any nursing work as a volunteer?
Yes
No
Q08: Do you work any hours for pay in a field other than nursing?
Yes (If Yes, Proceed To Q8a)
No (If No, Proceed To Q9)
Q08a: If yes, which of the following best describes your non-nursing position?
Part-Time
Full-Time
Per-Diem
April 2018 Technical Documentation for Licensure and Workforce Survey Data 15
Q9: If not currently employed for pay, please select the option that best describes your
status?
Seeking Work as a Nurse
Seeking Work in a Field Other Than
Nursing
Not Seeking Work at This Time
Retired
Not Applicable (I Am Employed For Pay)
Q10: Please indicate by selecting 'Yes' or 'No' if the following statements are reasons for not
seeking employment for pay (Select all that apply)
Taking care of home and family
Disabled/illness
Inadequate Salary
Currently enrolled in school
Difficulty in finding a nursing position
Other
Not applicable (I am employed for pay or
retired)
Q11: Are you actively employed for pay in nursing or in a position that requires a nursing
license?
Yes
No
Q12: In how many positions are you currently employed as a nurse?
1
2
3 Or More
Q13: Which of the following best describes your main nursing position? Your main position
is the one at which you work the most hours during your regular work year.
Part-Time
Full-Time
Per-Diem
Q14: How many hours do you work during a typical week in all your nursing positions?
0-80 (hours)
Q15: Number of weeks per year that you work in all your nursing positions, including paid
time off (year round employment = 52 weeks).
0-52 (weeks)
Q16: Please identify the type of setting that most closely corresponds to your main nursing
practice position.
Hospital
Nursing Home/Extended Care
Assisted Living Facility
Home Health
Correctional Facility
Academic Setting
Community Health
Healthcare Consulting/ Product Sales
Urgent Care/Walk-In Clinic
School Health Service
Occupational Health
Hospice
Ambulatory Care Setting
Insurance Claims/Benefits
Policy/Planning/Regulatory/Licensing Agency
Physician’s Office
Temporary/Staffing Agency
Public Health
Other
April 2018 Technical Documentation for Licensure and Workforce Survey Data 16
Q17: Please identify the position title that most closely corresponds to your main nursing
practice position.
Staff Nurse
Advance Practice Nurse
Nurse Executive/Administrator
Nurse Manager
Nurse Faculty
Quality Management/Risk Management
Utilization Review/Infection Control
Nurse Researcher (Non-Faculty)
Consultant
Travel Nurse
Case Manager
Educator
Other-Health Related
Other-Not Health Related
Q18: Please identify the employment specialty that most closely corresponds to your main
nursing practice position.
Critical Care
Adult Health/Family Health
Anesthesia
Community
Geriatric/Gerontology
Home Health
Maternal-Child Health
Oncology
Palliative Care
Pediatrics
Neonatal
Public Health
Psychiatric/Mental Health/Substance Abuse
Rehabilitation I Non-Psychiatric)
School Health
Emergency/Trauma
Women’s Health/Ob-Gyn
Information Technology
Operating Room/Peri-Operative
Other Acute Care
Other
Q19: Please indicate by selecting 'Yes' or 'No' if the following statements apply to your
nursing plans for the next 5 years. (Select all that apply)
Work as much as now
Reduce hours
Increase hours
Move into Florida
Move out of Florida
Leave nursing/retire
Other/Don't know
April 2018 Technical Documentation for Licensure and Workforce Survey Data 17
Appendix B: County Composition of FCN Regions