All of the analyses using facility data are based on North Carolina (NC) and North Dakota (ND). These datasets included a number of possible measures of shortage that could be used as dependent variables:

Effects of Nursing Shortage on Facility Operations. The surveys asked respondents an open-ended question about how nursing shortages have affected the operations of their facility. Responses were then coded into five broad categories: labor cost increase, reduced services, strain on staff, patient care problems, and organizational disturbance. More detailed codes within categories were also given (e.g., labor cost increase included breakouts for increases in agency use, recruitment costs, overtime, wages, retention expenses, development of float pools, and orientation expenses). This was an interesting variable because of in-depth discussions in the first advisory panel meeting about how the true measure of a nursing shortage should be related to patient care and facility operations. Although subjective, this variable touches on those issues. Caution was warranted, however, because the question asked about nursing shortage generally, and respondents may have answered the question thinking about LPNs as well as RNs, particularly if they were from a setting that relies heavily on LPNs (e.g., long-term care). Nonetheless, this variable was used as the dependent variable in a series of preliminary ordinary least squares (OLS) regressions.

RN Vacancy Rates. Both the NC and ND datasets included RN vacancy rates. Many facilities, however, had vacancy rates of 0, which limited the variation in the variable. Interestingly, there was very little correlation between RN vacancy rates and the number of reported effects of the nursing shortage, which was cause to question the utility of the consequences variable given its subjectivity. Vacancy rates were also used as the dependent variable in OLS regressions.

RN Turnover Rates. Turnover rates were not used in any of the in-depth analyses. In the first set of advisory panel meetings, the panelists pointed out that facilities that had a genuinely limited supply of RNs to draw from should be separated from facilities in which poor management led to large numbers of departures. Turnover can certainly reflect limited supply, but also seems likely to reflect problems of organizational culture, particularly in facilities that had low vacancy rates but high turnover (meaning that they had no trouble recruiting RNs, but had trouble retaining them.)

Time to Recruit RNs. Both datasets contained information on the average number of weeks reported to fill RN vacancies. Although theoretically a good indicator of shortage, the large amount of missing data for this variable ruled it out for practical reasons.

Difficulty Recruiting RNs. This ordinal variable was used in a series of ordered probit models conducted as part of the study. The variable used a five-point Likert scale with categories: Very Difficult, Difficult, Neutral, Easy, and Very Easy. Figure 6, which summarizes the responses for North Carolina, shows that somewhat more facilities reported difficulty than ease in recruiting RNs in 2004.

Figure 7 shows that only 4.6% of hospitals in North Carolina reported that recruiting RNs was either difficult or very difficult. The percentages were higher for home health agencies (15.2%), long-term care facilities (21.1%), and public health agencies (26.4%).

Figure 6. Facilities in North Carolina Reporting Different Levels of Difficulty Recruiting Nurses, 2004

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Figure 7. Percentage of Facilities in NC Reporting That Recruiting Nurses Was Either "Difficult" or "Very Difficult", 2004

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A. Preliminary Ordinary Least Squares (OLS) Regressions

OLS regression equations were estimated to predict and explain the number of adverse consequences and vacancy rates in all four types of facilities in North Carolina. First the models were estimated with both facility- and county-level explanatory variables, which was the ideal model. In recognition of the fact that facility-level variables were not available in most states, an abbreviated model using only county-level data was estimated for each facility type as well. The results for the models in which adverse consequences were the dependent variables are shown in Tables 2 through 6.

The results of these models were not particularly satisfying. Relatively few variables were strongly correlated to adverse consequences, and the explanatory power of the models (as measured by the R² statistic) was generally low. Although there were some statistically significant explanatory (independent) variables in the models for both predicted consequences and vacancy rates, the models explained only a relatively small percentage of the variation in the dependent variables. The explanatory power was even smaller when the facility-level variables (which would not be available outside of NC and ND without new data collection) were removed from the models, and only community variables were used.

The conclusion based on these models is that the variables collected by North Carolina were not adequate to accurately predict and explain either adverse consequences or vacancy rates.

Table 2. Coefficients for Full and Abbreviated OLS Regression Models to Predict Number of Adverse Effects of Nursing Shortages in Hospitals in NC

Full model R² = 0.429 Abbreviated model R² = 0.177

Table 3. Coefficients for Full OLS Regression Model to Predict RN Vacancy Rates in Nursing Homes in NC

R² = 0.35

Table 4. Coefficients for Full OLS Regression Model to Predict Number of Adverse Effects of Nursing Shortages in Home Health Agencies in NC

R² = 0.44

Table 5. Coefficients for Full OLS Regression Model to Predict Number of Adverse Effects of Nursing Shortages in Public Health Agencies in NC