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Staying the Course: Facility and Profession Retention Among Nursing Assistants in Nursing Homes

Department of Health Policy and Administration, University of North Carolina at Chapel Hill.

Address correspondence to Sally C. Stearns, PhD, University of North Carolina at Chapel Hill, Health Policy and Administration, CB#7411, McGavran-Greenberg, Chapel Hill, NC 27599-7411. E-mail: sally_stearns{at}unc.edu

	Abstract

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Objectives. High turnover rates among nursing assistants (NAs) in nursing homes have costly implications for facility operation and quality, and low rates of NA profession retention can deplete the stock of experienced staff. This study assessed the extent to which the same factors are associated with NAs' intent to leave a particular job versus the NA profession.

Methods. We used data for 2,328 NAs from the 2004 National Nursing Assistant Survey to model (a) two measures of facility retention (whether NAs expected to leave their current job within 1 year and whether they were also searching for a new job); and (b) NA profession retention, measured by whether NAs did not expect their next job to be as an NA.

Results. Substantially different factors affected facility versus profession retention. Facility characteristics (including supervisor qualities, training/safety, and benefits) primarily affected facility retention, whereas NA profession retention was negatively associated with income and education.

Discussion. Facilities can implement specific actions to retain NAs, though such policies may have a limited effect on retention in the profession. Broader enhancements of career opportunities may be necessary for profession retention, though balance between retention and promotion may be important.

Key Words: Long-term care • Intent to leave • Staffing • Turnover

DESPITE the growth of long-term care alternatives such as assisted living, nursing homes will continue to be an important source of health care and living arrangement for many of the nation's elders. One challenge faced by nursing homes is the task of hiring and retaining nursing assistants (NAs). Although turnover may in some cases reflect elimination of poor performers, NA turnover often results in replacement costs, lost productivity, compromised quality, and lowered morale (Brannon, Zinn, Mor, & Davis, 2002). One study estimated that up to 80% of the cost to replace employees is spent for indirect or hidden costs that include departing and incoming employee inefficiency and the cost of the position while vacant (Phillips, 1990). Furthermore, NA turnover can compromise quality of care (Cohen-Mansfield, 1997), and poor quality of care can exacerbate turnover (Anderson et al., 2005). The American Association of Homes and Services for the Aging (2007) reported that the average annual national turnover rate for NAs is 71% and that NA turnover costs more than $4 billion per year. An assessment of long-term care workforce issues (Institute for the Future of Aging Services, 2007) cited current vacancy rates of 12% for NAs in nursing homes and predicted substantial future increases in shortages given the aging of the population.

Facility and profession turnover may happen for a variety of reasons, both random and purposeful. The NA profession is characterized by relatively poor wages and benefits, and the nature of work is both physically and emotionally demanding. These factors cause recruitment and retention problems that manifest themselves in high turnover rates (Reinhard & Stone, 2001). Studies have shown that in addition to compensation and benefits, NAs are highly concerned with job security, interactive aspects of their jobs, involvement in care planning and work-related decisions, and professional growth potential (Atchison, 1998; Bowers, Esmond, & Jacobson, 2003; Parsons, Simmons, Penn, & Furlough, 2003). Leaving a job or the NA profession generally requires an alternative employment option for the worker, and measures such as intent to leave are not always predictive of actual departures from a facility. Some studies have focused on factors associated with job satisfaction or organizational commitment, defined as the relative strength of an individual's linkage to the employing organization. In an extensive review of the literature, Wagner (2007) identified intent to leave as an important antecedent of turnover as well as an important mediating variable in modeling the effect of organizational commitment on turnover.

Although many assessments of job satisfaction or turnover have used only facility-level data or have involved small samples of individuals from a limited number of facilities or states, recent work has expanded researchers' understanding of factors associated with facility turnover. Castle (2005) found that a 10% increase in turnover among top management is correlated with a 21% increase in the odds that a facility will have high NA turnover. Kash, Castle, Naufal, and Hawes (2006) concluded that NA turnover is reduced by higher administrative expenditures and higher NA wages. Castle and Engberg (2006) found that lower staffing levels, lower quality, for-profit ownership, and higher bed size were associated with higher NA turnover. Donoghue and Castle (2006) found that factors such as nurse-to-bed ratio, quality-of-care deficiencies, and case mix predict voluntary and involuntary turnover differently. Using surveys of nursing aides at facilities in five states, Castle, Engberg, Anderson, and Men (2007) hypothesized that job satisfaction is an important antecedent of both intent to leave and turnover; their study also showed that training, benefits, and work schedule were predictive of intent to leave, and that, in addition to these factors, facility quality of care and workplace support were predictive of turnover.

Although researchers have studied factors associated with NA facility turnover, the literature investigating the reasons why NAs leave their profession is sparse. Stone and Weiner (2001) concluded that, in addition to facility-specific experiences, other factors as varied as societal perceptions of the occupation, local economic conditions, and public policies affect the choice to remain in the profession. Hiring an NA without prior experience is likely to be more costly in terms of initial training required than hiring an experienced NA. If the quality of care provided by experienced NAs is higher than the quality provided by novice NAs, then patient outcomes may be improved through increased professional retention. More generally, NAs who plan to leave the field in the future may be less committed to their work than those whose long-term career plans revolve around continuing to being an NA. As with facility turnover, small rates of departure from the NA profession may be efficient, and some progression to higher positions in long-term care may be desirable. Yet high levels of departure from the NA profession detract from the possibility of accruing a competent and experienced employee pool.

This analysis used data from the 2004 National Nursing Assistant Survey (NNAS) to investigate NA retention in the current job as well as the profession as measured by intent to leave. Figure 1 summarizes the conceptual model for this study, which was closely aligned with the model used by Castle and colleagues (2007) except that we viewed job satisfaction and organizational commitment as being jointly determined with intent to leave. This distinction is important, because Castle and colleagues specified job satisfaction as an antecedent to intent to leave but did not find intent to leave to be separately predictive of actual turnover once job satisfaction was controlled. Identification of the separate effects of job satisfaction and intent to leave is simply not possible with cross-sectional surveys of NAs. The goal of this study was to determine whether the factors that predict NAs' intent to leave their current job at a facility also predict their intent to leave the NA profession. We hypothesized that factors unique to a facility (e.g., supervisor/scheduling) would predict intent to leave the current job/facility, whereas factors related to both the current job and other jobs (e.g., the NA's initial training or socioeconomic characteristics) would predict intent to leave the profession.

View larger version (12K): [in this window] [in a new window]   Figure 1. Conceptual model of nursing assistant (NA) facility and profession turnover

	METHODS

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The NNAS was designed to provide, among other things, information on factors that contribute to NAs' job satisfaction and likelihood of staying in their jobs (Squillace, Remsburg, Bercovitz, Rosenoff, & Branden, 2006). From the original 2004 National Nursing Home Survey (NNHS) sample of 1,500 facilities, 790 nursing homes were selected to participate in the NNAS. In all, 76% (582 homes) provided contact information for NAs working directly for the facilities. The NNAS excluded NAs not already certified or in training for certification unless they were trained prior to 1987, contract NAs, or assistants working fewer than 16 hr per week. A total of 3,017 (71%) of 4,542 eligible NAs completed an interview, resulting in a combined facility and person response rate of 53%. The survey was administered when NAs were not at work to minimize fears of job loss or other negative reprisals given the potentially sensitive nature of the work-related questions.

The NNAS is the first national survey gathering NA data at the individual level. Person-level data allow for more information and precision than facility observations in analyzing factors that affect NA facility or profession retention, and the availability of multiple respondents per facility strengthens the ability to identify individual and facility factors associated with retention. The model was as follows:

where i represents the individual NA respondent and h represents the nursing home. The model used three dichotomous self-reported measures of intent to leave, y_ih. Two measures reflecting intent to leave the facility were (a) a broad measure of whether the respondent was very or somewhat likely to leave his or her current job in the next year and (b) a stronger measure of whether the respondent was actively searching for a job in addition to being very or somewhat likely to leave his or her current job within 1 year. Although some NAs may seek promotion to a different job within the same facility, we expected that most workers who expected to leave their current job also expected to leave their current facility. The second measure represented a stronger stage of intent to leave and was likely to be more highly correlated with subsequent departures from the facility. We measured intent to leave the NA profession by whether the NA did not think his or her next job would be as an NA but instead would be as something else. We interpreted this variable as referring only to being an NA in a nursing home, because that was the surveyed population; an individual may still have intended to remain in the field of long-term care. The error term consisted of one component, µ_h, which only varied between facilities; and another component, _ih, which varied across facilities and individual respondents.

Following prior studies of turnover (Castle et al., 2007; Price, 2001), the three vectors of explanatory variables included characteristics related to the working environment and facility as reported by the NA, NA personal characteristics, and other facility characteristics. Personal characteristics included gender, race, Hispanic ethnicity, age, marital status, having at least one child in the household, citizenship status, whether the NA worked at the facility full time, the commute time in minutes (for the facility intent-to-leave models only), and income and education. The remaining factors consisted of characteristics of the work setting as well as area characteristics that were external to both the individual and the organization. For this study, characteristics of the work setting had two important dimensions: whether they were observed or not observed, and whether the observed factors were constant (or were reported as constant) within a facility for all NAs.

Three categories of measures that have been identified as important in prior studies and were reported by NAs in the NNAS include supervision quality/scheduling, training/safety, and benefits (Castle et al., 2007). All of these measures had at least some variation across NAs within facilities. Although some within-facility variation may have been due to erroneous knowledge on the part of the NA (e.g., whether the facility has lifting devices available) or employment status (e.g., health insurance may not have been available to part-time workers), the worker's perceptions of these measures or choices available to him or her were potentially important in modeling intent to leave the facility or profession.

We used three measures of supervision quality/scheduling. The NNAS has a series of 10 questions assessing the quality of the supervisor (whether the supervisor provides clear instructions, treats all NAs equally, deals with NA complaints and concerns, is open to new ideas, is supportive of progress in the NA's career, helps NAs with tasks when needed, listens when the NA is worried about resident's care, supports NAs working in teams, disciplines NAs not performing well, and tells the NA when doing a good job); we constructed an index as a count of the number of times the NA strongly agreed with these statements. We also included a measure of whether the NAs felt they were respected a great deal by their supervisors and a scheduling measure of whether they had enough or more than enough time for all of their duties. Although research has shown overall training to be important (Iowa CareGivers Association, 2000), training related to preventing injuries may be particularly relevant. NAs have among the highest back and shoulder injury rates in the United States (Myers, Silverstein, & Nelson, 2002), and nursing homes can provide training or assistive devices to reduce the injury rates (Garg, 1999). We therefore included the following five measures related to NAs' training/safety experience: whether NAs felt their initial training (which may or may not have occurred at the facility) prepared them well for working in a nursing home, whether NAs perceived their initial training as excellent or good in preventing work injuries, whether NAs had a lot of say in topics covered in classes paid for or offered by the current employer, whether the facility always had lifting devices available, and whether NAs had had at least one work-related injury in the past. Measures of benefits provided by the facility included: the hourly pay rate; whether the NA got paid time off for holidays, vacation/personal days, or sick days; and whether health insurance was offered.

In addition to the NA-reported measures, the existing literature has shown that a number of additional facility characteristics (e.g., other aspects of management/administration and work environment, case mix, quality-of-care staffing turnover, occupancy rate, organizational policies, growth opportunities, reputation, etc.) and area characteristics (e.g., unemployment, wages/benefits in the market, etc.) are important determinants of facility turnover (Banaszak-Holl & Hines, 1996; Castle & Engberg, 2006; Castle et al., 2007; Harrington & Swan, 2003). Public use versions of the NNHS and NNAS cannot be merged due to confidentiality concerns, however, and only three facility variables (ownership, bed size, and urban/rural location) from the NNHS are included in the public use versions of the NNAS. We tested facility fixed effects (FE) models to account for all facility characteristics (observed or unobserved) that did not vary across observations for all NAs at each facility, thereby providing strong protection against omitted facility variable bias.

In the FE models, the estimated coefficients for the NA-reported measures reflected variation in intent to leave in relation to within-facility variation in a variable. For example, the facility fixed effects controlled for the cost of labor in each facility's market area and any between-facility variation in wages, whereas the coefficient for the wage rate reported by the NA captured the effect of any within-facility variation in wages on the likelihood of retention. Although the limited number of NA respondents per facility meant that the facility coefficients would not necessarily be representative of the specific facility relationship with the dependent variable in a population sense, the availability of within-facility variation in the measures enabled estimation of the NA-reported variables in the model as well as the determination of the role of facility characteristics as a whole. However, the limited number of NA observations may have precluded precise estimation of the effect of some of these variables separately from the facility fixed effects.

Although we could have used a logit model, estimation of facility fixed effects using a logit model is problematic due to lack of variation in the intent-to-leave variables among NAs at some facilities. We therefore estimated linear probability models (LPM) using ordinary least squares and linear facility FE models to determine the importance of controlling for unobserved facility characteristics. We dealt with missing data by using complete case analysis of observations with valid responses for all model variables. We estimated preliminary versions (logit models without fixed effects, and subgroup analysis using full-time workers only or only workers who expected to stay in the profession) to test the robustness of the models. We estimated final models by using survey weights and robust standard errors.

	RESULTS

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Analysis Sample and Descriptive Statistics
A total of 120 NNAS respondents left their facilities before the survey was administered and were given a different survey that did not include many of the measures used in this analysis. The main reasons for further exclusions were missing responses on paid sick days (8.6%) and injury (6.7%). After exclusions for missing data, the final analysis sample consisted of 2,328 NAs at 577 nursing homes. The number of NAs at each facility ranged from one to eight; the analysis sample had an average of four NAs per facility, and 32 facilities had only one NA respondent.

Table 1 includes population-weighted statistics for 120 respondents who quit prior to the full survey, the 2,897 respondents still employed by the facilities at the time of the survey, and the 2,328 respondents in the complete case analysis sample. Compared to the 2,897 stayers, the 120 leavers tended to be younger, have lower income and wages, and be more likely to have worked at a for-profit facility. The ability to control for these factors helped to ensure that response bias did not affect our estimates, though concern about any unobserved differences between our analysis sample and the full population of NAs remains. The descriptive statistics were very similar for the stayers and the NA analysis sample, and they reflected usual characteristics for NAs (e.g., primarily female, younger than age 45, household income less than $30,000, and the majority having a high school education but no further degree).

Weighted statistics for the retention measures in the analysis sample showed that 43% of respondents said it was very or somewhat likely that they would not be in their current job 1 year later, whereas 20% said that they were very or somewhat likely not to be in their current job 1 year later and that they were actively looking for a job. Nearly half (48%) did not expect their next job to be as an NA. Even though intent to leave may not always result in turnover, these statistics are consistent with the high documented rates of turnover in the industry. The NNAS asked respondents who were very or somewhat likely to leave their reasons for leaving; the six most important reasons indicated by these 1,062 respondents (shown in Table 1) were poor pay, already having a new or better job, problems with working conditions/policies, too many residents to care for, poor benefits, or problems with supervisor. The ranking of reasons was similar regardless of whether the person also planned to leave the profession.

Surprisingly little correlation existed between the measures of NA intent to leave the facility and the profession, as shown by the distribution of observations in Table 2. Although a chi-square test showed that the broad intent-to-leave facility measure and intent to leave the profession were correlated (p =.026), roughly 56% (555/1,068 or 713/1,260) expected their next job to be as an NA regardless of whether they expected to leave the current job. Regardless of whether respondents expected their next job to be as an NA, roughly 48% (513/1,060) or 43% (555/1,268) were very or somewhat likely to leave their current job in the next year. The similarities in these distributions suggested that very different processes may have been driving these decisions.

A few of the personal characteristics were significantly related to the two measures of intent to leave the facility. For example, younger respondents were more likely to be actively searching for a job in addition to reporting an intention to leave their current job, whereas Hispanics were less likely to be actively searching for a job and non-Whites were more likely to intend to leave the profession. Having more education was positively associated with a greater intent to leave the facility (broad measure) and profession; the effects were especially strong for intent to leave the profession. Having income greater than $50,000 was strongly associated with intent to leave the profession but not with either of the measures of intent to leave a particular job/facility.

The results from the specification tests showed that many facility characteristics (observed and unobserved) were significantly associated with intent to leave a particular facility, as evidenced by the substantially higher R-squared values relative to the LP model (results not shown). For the LP model for intent to leave the NA profession, only for-profit status was significantly and negatively associated with intent to leave.

Our robustness checks showed that (a) logit without fixed effects versus LP models and (b) models with full-time workers only had results that were extremely similar to the results in Table 4 in terms of magnitude, sign, and significance of coefficients. Estimations using only workers who expected to stay in the profession had results that were similar in magnitude to the results in Table 4, though modest shifts in magnitude and significance occurred, possibly because of the substantially reduced sample size.

	DISCUSSION

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The key finding from this analysis, which contributes to the literature substantially by studying factors associated with NA profession retention, is that the factors determining facility and profession retention are very different. Facility retention is affected by a number of NA-reported facility characteristics (supervision/scheduling, training/safety, and benefits); in contrast, only NA reports of poor scheduling and initial training are borderline indicative of intent to leave the NA profession. Higher education leads to greater likelihood of intent to leave facility and profession. Higher income, being of White race, and being of age 46 to 60 (relative to being older than age 60) all reduce the likelihood of intending to leave the NA profession. Facility and area characteristics (observed and unobserved) have strong effects on the likelihood of intending to stay in a current job.

The different relationships detected by the analysis are quite plausible. The index of supervisor qualities may represent the degree to which the NA feels that facility administration respects the NAs' opinions, directly affecting the propensity to stay at a particular job or facility. Conversely, such a measure would not necessarily affect a decision to continue seeking employment as an NA in the future. The same logic holds for availability of lifting devices; the lack of statistical significance in the profession regression suggests that the NAs perceive that lifting device availability is a feature of an individual nursing home and not the profession itself. It is possible, of course, that lift availability may serve as a general proxy for other important aspects of facility quality. It is surprising that the scheduling measure of whether the NA had enough time to complete tasks was not significant in the facility intent-to-leave regressions and was of only borderline significance in the intent to leave the NA profession model because staff-to-resident ratios are cited as low in many facilities (Harrington, 2005). These factors contrast with the educational status and household income of NAs, which may reflect the degree of opportunity to seek employment in alternative facilities or professions. The fact that benefit measures were not significant in explaining profession retention may be due to the fact that job benefits for NAs were not modeled in relation to other alternative occupations.

The results presented in this article are subject to some limitations. First, missing data may have led to biased coefficient estimates. Some NAs (n = 120) were not included because they left their facility before responding to the survey, and data for NAs from only 577 of the original target sample of 790 nursing homes were available for the analysis. It was not possible to adjust for these omissions, which, when combined with additional deletions due to missing responses, resulted in an effective response rate of less than 50%. Still, the NNAS represents the first nationally representative survey assessing intent to leave either the current job or the NA profession. Controlling for a number of observed measures and using weights in the analysis may have helped counter any bias from the low response rates. Although the availability of only a few facility measures (ownership, size, and location) on the NNAS might seem to be a limitation, the ability to use facility fixed effects provides for strong control for all facility-specific factors.

In total, the results are interesting and potentially important for policy. In light of continued growth in the elderly population in the United States, establishing and maintaining a well-qualified NA workforce may be even more important in the future than it is today. A recent report considered a range of problems pertaining to the long-term-care workforce and provided an excellent summary of the situation:

The dilemmas peculiar to the recruitment and retention of the paraprofessional workforce are perhaps the most complex and difficult to resolve. Wages are not adequate to support young families with children. The job is often not well-designed, creating inefficiencies, unnecessary job burdens and subjecting occupants to high rates of injury. There are few opportunities for career advancement. Supervision is poor or non-existent. In addition, low unemployment rates for all entry-level personnel, coupled with increasing levels of education among minority populations, provide this labor pool with far more choices than low-income women have had in the past. (Institute for the Future of Aging Services, 2007, p. 9)

Our study results reinforce these points, ranging from the effects of supervision quality on the intent to leave the current job/facility to the relationships between education and income to the intent to leave the NA profession. As new policies are promulgated, it is important to know that facilities may undertake specific actions to reduce their own turnover, but it is more problematic to know that facility-specific policies have relatively little bearing on NAs' decisions to remain in the profession. The same report identified five goals to address the problems cited above: "(1) expand the supply of personnel coming into the long-term care field; (2) create more competitive long-term care jobs through wage and benefit increases; (3) improve working conditions and the quality of longer-term care jobs; (4) make larger and smarter investments in the development and continuing education of the long-term care workforce; and (5) moderate the demand for long-term care personnel" (Institute for the Future of Aging Services, 2007, p. 13). The results from the present study do not address the first or fifth goals, except to note that it does not make sense to increase the supply of people coming into the field unless rates of NA profession retention can be increased. Yet the results do reinforce the importance of the second, third, and fourth recommendations, which are accompanied by proposals of specific initiatives, many of which are relevant for the NA workforce.

For example, initiatives such as Better Jobs Better Care (www.bjbc.org) that seek to achieve changes in long-term-care policy and practice to reduce high vacancy and turnover rates represent a major step toward this goal. Actions such as empowering NAs regarding input in their training needs and safety protections can be beneficial in reducing intent to leave or turnover and subsequently improving quality of care. The significance of facility fixed effects most likely indicates the importance of factors such as good facility management in improving facility retention of NAs. Yet the findings regarding income and education mean that financial compensation and benefits for NAs must be competitive relative to other occupations to improve not only facility retention but also profession retention. For profession retention, balance between retention and promotion along a more encompassing career ladder is undoubtedly important.

The NNAS is a very rich source of other information not analyzed in this study, including what respondents were doing prior to becoming NAs, whether they would become NAs again now that they have experience with the job, and information on prior jobs. Researchers could make more detailed assessments of the relation between other measures and intent to leave the current job. Yet the fact that almost half of the respondents do not expect their next job to be as an NA raises questions pertaining to what the respondents expect to do next that the NNAS cannot easily answer. For example, do respondents plan to take time to raise a family, return to school, seek training for an alternative occupation within health care or long-term care (e.g., becoming a licensed nurse), or seek an alternative occupation outside of health care or long-term care? Uncovering answers to these questions, which may require panels of people working in other low-wage industries as well as long-term care, is important in order to stem departures from the NA profession and to develop efficient career trajectories for NAs in nursing homes.

	Footnotes

 
Decision Editor: Kenneth F. Ferraro, PhD

Received for publication August 30, 2007. Accepted for publication January 7, 2008.

	References

TOP Abstract Methods Results Discussion References

 

• American Association of Homes and Services for the Aging. (2007). Aging services: The facts. Retrieved April 21, 2007, from www.aahsa.org/aging_services/default.asp.
• Anderson, R. A., Ammarell, N., Bailey, D., Colon-Emeric, C., Corazzini, K. N., Lillie, M., et al. (2005). Nurse assistant mental models, sensemaking, care actions, and consequences for nursing home residents. Qualitative Health Research, 15,1006-1021.[Abstract/Free Full Text]
• Atchison, J. H. (1998). Perceived job satisfaction factors of nursing assistants employed in Midwest nursing homes. Geriatric Nursing, 19,135-138.
• Banaszak-Holl, J., Hines, M. A. (1996). Factors associated with nursing home staff turnover. The Gerontologist, 36,512-517.[Abstract]
• Bowers, B., Esmond, S., Jacobson, N. (2003). Turnover reinterpreted: CNAs talk about why they leave. Journal of Gerontological Nursing, 29,(3), 36-43.[Medline]
• Brannon, D., Zinn, J. S., Mor, V., Davis, J. (2002). An exploration of job, organizational, and environmental factors associated with high and low nursing assistant turnover. The Gerontologist, 42,159-168.[Abstract/Free Full Text]
• Castle, N. G. (2005). Turnover begets turnover. The Gerontologist, 45,186-195.[Abstract/Free Full Text]
• Castle, N. G., Engberg, J. (2006). Organizational characteristics associated with staff turnover in nursing homes. The Gerontologist, 46,62-73.[Abstract/Free Full Text]
• Castle, N. G., Engberg, J., Anderson, R., Men, A. (2007). Job satisfaction of nurse aides in nursing homes: Intent to leave and turnover. The Gerontologist, 47,193-204.[Abstract/Free Full Text]
• Cohen-Mansfield, J. (1997). Turnover among nursing home staff. Nursing Management, 28,59-64.[Medline]
• Donoghue, C., Castle, N. G. (2006). Voluntary and involuntary nursing home staff turnover. Research on Aging, 28,454-472.[Abstract/Free Full Text]
• Garg, A. (1999). Long-term effectiveness of "Zero-Lift Program" in seven nursing homes and one hospital. Retrieved April 24, 2007, from www.aft.org/topics/no-lift/Zero_Lift_Report.pdf.
• Harrington, C. (2005). Nurse staffing in nursing homes in the U.S. Journal of Gerontological Nursing, 31,(2), 18-23.[Medline]
• Harrington, C., Swan, J. H. (2003). Nursing home staffing, turnover, and case mix. Medical Care Research and Review, 60,366-392.[Abstract/Free Full Text]
• Institute for the Future of Aging Services. (2007). The long-term care workforce: Can the crisis be fixed? Problems, causes and options. Washington, DC: Author.
• Iowa CareGivers Association. (2000). Certified nursing assistant recruitment and retention pilot project final report. Retrieved January 3, 2008, from www.iowacaregivers.org/uploads/pdf/DHSreport.pdf.
• Kash, B. A., Castle, N. G., Naufal, G. S., Hawes, C. (2006). Effect of staff turnover on staffing: A closer look at registered nurses, licensed vocational nurses, and certified nursing assistants. The Gerontologist, 46,609-619.[Abstract/Free Full Text]
• Myers, D., Silverstein, B., Nelson, N. A. (2002). Predictors of shoulder and back injuries in nursing home workers: A prospective study. American Journal of Industrial Medicine, 41,466-476.[Medline]
• Parsons, S. K., Simmons, W. P., Penn, K., Furlough, M. (2003). Determinants of satisfaction and turnover among nursing assistants: The results of a statewide survey. Journal of Gerontological Nursing, 29,(3), 51-58.[Medline]
• Phillips, D. J. (1990). The price tag on turnover. Personnel Journal, 69,(2), 58-61.
• Price, J. (2001). Reflections on the determinants of voluntary turnover. International Journal of Manpower, 22,600-624.
• Reinhard, S., Stone, R. (2001). Promoting quality in nursing homes:. The Wellspring model. Retrieved from www.cmwf.org/usr_doc/reinhard_wellspring_432.pdf.
• Squillace, M. R., Remsburg, R. E., Bercovitz, A., Rosenoff, E., Branden, L. (2006). An introduction to the National Nursing Assistant Survey. Retrieved August 22, 2007, from http://aspe.hhs.gov/daltcp/reports/2006/NNASintro.pdf.
• Stone, R., Weiner, J. (2001). Who will care for us? Addressing the long-term care workforce crisis. Retrieved August 27, 2007, from aspe.hhs.gov/daltcp/reports/ltcwf.pdf.
• Wagner, C. M. (2007). Organizational commitment as a predictor variable in nursing turnover research: Literature review. Journal of Advanced Nursing, 60,(3), 235-247.[Medline]
• Wooldridge, J. (2006). Introductory econometrics: A modern approach. New York: Thomson/South-Western.

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