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The Gerontologist 48:60-70 (2008)
© 2008 The Gerontological Society of America

The Impact of Stress and Support on Direct Care Workers' Job Satisfaction

Farida K. Ejaz, PhD1, Linda S. Noelker, PhD1, Heather L. Menne, PhD1 and Joshua G. Bagaka's, PhD2

Correspondence: Address correspondence to Farida K. Ejaz, Margaret Blenkner Research Institute, Benjamin Rose Institute, 11900 Fairhill Road, Suite 300, Cleveland, OH 44120-1053. E-mail: fejaz{at}benrose.org


    Abstract
 TOP
 Abstract
 Conceptual Model
 Methods
 Results
 Discussion
 References
 
Purpose: This research applies a stress and support conceptual model to investigate the effects of background characteristics, personal and job-related stressors, and workplace support on direct care workers' (DCW) job satisfaction. Design and Methods: Researchers collected survey data from 644 DCWs in 49 long-term care (LTC) organizations. The DCWs included nurse assistants in nursing homes, resident assistants in assisted living facilities, and home care aides in home health agencies. We examined the influence of components of the LTC stress and support model on DCW job satisfaction. Initially, we ran a multiple regression analysis by entering individual-level DCW predictors with job satisfaction as the outcome. Subsequently, we used hierarchical linear modeling to examine the influence of organizational factors on DCW job satisfaction after controlling for significant individual-level DCW variables. Results: Components of the model explained 51% of the variance in DCW job satisfaction. Background characteristics of DCWs were less important than personal stressors (e.g., depression), job-related stressors (e.g., continuing education), and social support (e.g., interactions with others) in predicting job satisfaction. Results from hierarchical linear modeling analysis showed that nursing homes compared to the two other types of LTC organizations had lower average DCW job satisfaction rates, as did organizations offering lower minimum hourly rates and those reporting turnover problems. Implications: Study findings underscore the importance of targeting both DCW-level and organizational-level factors to increase DCW job satisfaction.

Key Words: Aides in nursing homes • Home health agencies • Assisted living


Aside from family members, direct care workers (DCWs) such as nurse assistants in nursing homes, resident assistants in assisted living facilities, and home care aides in home health agencies provide the majority of hands-on care for chronically impaired older adults. These workers in the long-term care (LTC) industry help physically and cognitively impaired individuals with tasks such as bathing, dressing, toileting, and feeding, thus making them the foundation of the LTC workforce. However, turnover of DCWs ranges from 45% to more than 100% and costs the industry nearly $4.1 billion annually (Harris-Kojetin, Lipson, Fielding, Kiefer, & Stone, 2004).

Turnover among DCWs stems from a variety of factors, key among which is job dissatisfaction (Capitman, Leutz, Bishop, & Casler, 2004; Castle, Degenholtz, & Rosen, 2006; Institute of Medicine, 2001; National Commission on Nursing Workforce for Long Term Care, 2005; Nursing workforce: Recruitment, 2001; Parsons, Simmons, Penn, & Furlough, 2003; Wiener, 2003). Given the extent of DCW turnover, gaining a better understanding of the factors affecting job satisfaction is critical in order to develop and implement workplace interventions to enhance job satisfaction. Prior research studies on the determinants of job satisfaction among DCWs are sparse. Furthermore, they are limited by the general absence of a conceptual framework to guide the research or one that is narrow in scope, a failure to consider both DCW-level and organizational-level factors to simultaneously address the issue of nested data, and the lack of inclusion of DCWs from different types of LTC settings (Castle et al., 2006). We designed this study to address these limitations. It was guided by the LTC stress and support model, and we collected data from DCWs in three major sectors of the LTC industry and from study site liaisons on organizational issues. Furthermore, we used hierarchical linear modeling to identify organizational effects on the study outcome while controlling for data obtained at the individual level from DCWs.


    Conceptual Model
 TOP
 Abstract
 Conceptual Model
 Methods
 Results
 Discussion
 References
 
The study's conceptual framework was adapted from the stress process model, which examines the common sources of stress and its negative impact on family caregivers with impaired older relatives living in home- and community-based settings (Aneshensel, Pearlin, Mullan, Zarit, & Whitlatch, 1995; Pearlin, Mullan, Semple, & Skaff, 1990; Zarit & Whitlatch, 1992). The sources of stress and support in that model were later adapted for use with DCWs in nursing homes (Cohen-Mansfield & Noelker, 2000; Noelker, Ejaz, Menne, & Jones, 2006) and then DCWs in assisted living facilities and home health agencies (Ejaz & Noelker, 2006). This adapted LTC stress and support model was developed based on the similarities in caregiving tasks by family members and DCWs. Both provide the majority of hands-on care for elders either in the community or in LTC settings and perform tasks that involve intensive physical and emotional labor (Noelker, 2001; Whitlatch, Schur, Noelker, Ejaz, & Looman, 2001). An important adaptation of the LTC stress and support model is the inclusion of organizational variables because, unlike family caregivers, DCWs are employed by organizations with differing characteristics and practices.

We did not use other conceptual models in the study because of their limitations. One study used a conceptual framework organized around staff demographic characteristics, job characteristics, and work environments to predict employee job satisfaction, job commitment, and turnover intent in a single type of LTC setting (Karsh, Booske, & Sainfort, 2005). Another study on factors distinguishing nursing home facilities with high and low DCW turnover used job, organizational, and environmental factors at the organizational level but did not include any individual-level DCW characteristics (Brannon, Zinn, Mor, & Davis, 2002). Thus, the proposed research responded to the need for a more inclusive conceptual framework to empirically test how individual-level DCW characteristics and stressors as well as organizational characteristics and management issues influence job satisfaction.

Elements of the Conceptual Model
At the DCW level, variables in the model (see Figure 1) include the background characteristics of the DCWs (A), their personal and job-related sources of stress (B), and workplace support (C). At the organizational level (D), the model includes characteristics of the organizations (e.g., type of LTC setting) and management issues (e.g., high DCW turnover). The various sources of stress and support at both the DCW and organizational levels influence the study outcome (E), that is, DCW job satisfaction.


Figure 01
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Figure 1. Long-term care (LTC) stress and support model predicting direct care worker (DCW) job satisfaction. Additional measures of components in the model can be found in the Measures section

 
DCW Individual-Level Predictors of Job Satisfaction
Background Characteristics
Research on the stress process has indicated that background and contextual features have an important bearing on outcomes (Aneshensel et al., 1995). A large portion of the DCW workforce is composed of low-income, unmarried minority women with dependent children (Yamada, 2002). Consequently, we included age, race, and marital status in the model.

Personal Stressors
Because DCWs are the lowest paid workers in the health care labor force, financial and family worries are likely to be personal sources of stress that can carry over to the work setting (Stone & Weiner, 2001; Tellis-Nayak & Tellis-Nayak, 1989). In addition, because the care DCWs give to the frail elderly is physically and emotionally demanding, over time it is likely to negatively affect their physical and emotional health (Mutaner et al., 2004; Noelker & Harel, 2000; Stone & Weiner, 2001).

Job-Related Stressors
Stressors on the job include job design features such as the frequency of scheduling changes (e.g., coming in early, staying late, being called to work on a day off) and lack of permanent assignments. Scheduling changes are likely to disrupt the practice of permanent assignment to residents or clients and are indicative of short staffing and turnover problems, which in turn impact overall job satisfaction. Another important component of job-related stress is inadequate on-the-job training. Such training includes orientation to the job, peer mentoring, and continuing education. Other job-related stressors investigated here are poor pay and lack of benefits such as health insurance (U.S. Department of Health and Human Services, 2003, 2004; U.S. General Accounting Office, 2001).

Support in the Workplace
Research has provided evidence that positive support can attenuate the negative effects of stress (Bass, Noelker, & Rechlin, 1996), whereas negative support can exacerbate stress (Krause, 1995). For example, DCWs often hear racist and ethnic remarks made about them by clients, families, and other staff (Berdes & Eckert, 2001; 2007; Foner, 1994; Tellis-Nayak & Tellis-Nayak, 1989). Using the LTC stress and support model, we investigated the effects of DCW perceptions of workplace racism and other negative and positive interactions on job satisfaction.

Organizational-Level Predictors of DCW Job Satisfaction
At the organizational level, a key background characteristic is type of LTC setting. Examining differences by type of LTC setting is important because settings can significantly differ in their structure and operation (Ejaz et al., 2006). For example, DCWs in nursing homes work in a more institutional setting compared to home care aides, who provide care in a client's home. Research has also shown that staffing patterns and working conditions in for-profit facilities place greater demands on nursing staff (Anderson, Issel, & McDaniel, 1997).

Other organizational features have the potential to influence worker outcomes, including the availability of resources that are affected by reimbursement streams (i.e., Medicare, Medicaid, and private pay). A study by Mor, Zinn, Angelelli, Teno, and Miller (2004) indicated that organizations serving primarily Medicaid and minority residents have fewer resources (e.g., lower reimbursement rates under public programs), which can negatively impact staffing levels, rate of pay, amount of staff training, and availability of equipment and supplies. Another key organizational characteristic is the difficulty an organization faces because of high DCW turnover rates (Harris-Kojetin et al., 2004). Based on these prior studies, the LTC stress and support model also included the following organizational-level predictors: the percentage of minority and nonminority clients served; the percentage of reimbursement under Medicare, Medicaid, and private pay; and the difficulty the organization reports with DCWs quitting or being fired.


    Methods
 TOP
 Abstract
 Conceptual Model
 Methods
 Results
 Discussion
 References
 
Research Questions
The research addressed the following questions:

  1. To what extent do DCW reports of personal and job-related stress and workplace support predict their job satisfaction?
  2. To what extent do organizational characteristics and management issues predict DCW job satisfaction, after controlling for DCW individual-level variables?

Site Selection
We developed a database of all nursing homes, assisted living facilities, and home health agencies in a five-county area of northeast Ohio by obtaining information from various sources (e.g., the state's Department of Health, the local area agency on aging, and the state's assisted living association). The comprehensive list included 75 home health agencies, 143 nursing homes, and 101 assisted living facilities, for a total of 319 agencies. Because the list of LTC facilities was obtained from state agencies, it excluded organizations that were not certified or licensed (e.g., private home health agencies). Following the creation of a database of the 319 facilities, we contacted these organizations to determine if they met the criteria for inclusion in the study: (a) facilities with more than 10 DCWs and (b) facilities that employed their own DCWs and did not use only agency staff or outside contractors. Of the 319 agencies in the study area, we were able to obtain information from 217 (68%). Based on the information supplied by the 217 organizations, a total of 161 (29 home health agencies, 40 assisted living facilities, and 92 nursing homes) met the study criteria.

We determined that, in order to effectively address Research Question 2, we would need a minimum of 30 organizations to investigate the effects of organizational-level variables while controlling for DCW-level variables (Kraemer & Thiemann, 1988). We used proportionate random sampling techniques (Cochran, 1977) to select a larger number of sites than the minimum number required. This sampling strategy allowed us to randomly select 8 home care agencies, 13 assisted living facilities, and 25 nursing homes, for a total of 46 sites.

In view of the project's restricted time frame for site and sample recruitment and data collection, we assumed a 100% refusal rate to ensure that the replacement pool contained enough sites. Thus, we contacted a total of 90 sites (see Table 1), and 41% of those refused to participate. Refusals included active and passive refusals, the latter referring to cases in which facility administrators agreed to participate but did not follow up with the research team. Refusal rates differed by type of LTC setting. Almost half (49%) of the nursing homes contacted were refusals, whereas only one third (33%) of home care agencies were. In the end, 46 facilities participated in the study. Ultimately, we added three additional sites to boost the overall number of DCWs in the sample (see "Respondent Selection"). Comparisons between participating and nonparticipating sites revealed that sites did not differ on key variables such as type of LTC setting, number of clients/residents served, or the number of DCWs that they employed.


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Table 1. Response Rates.

 
Respondent Selection
We determined that, in order to have adequate power of.80 or greater, with an effect size of.05 and approximately 21 predictors in the model, we would need a minimum sample size of 615 DCWs to address Research Question 1 (Murphy & Myors, 1998). We used proportionate random sampling techniques to recruit the required number of DCWs from the respective sites.

Because of the limited time frame for data collection and in anticipation of a high DCW refusal rate, we contacted a total of 1,058 DCWs (see Table 1). Of these, 39% refused to participate. We completed a total of 644 interviews, which was more than the minimum number of DCWs required to address Research Question 1. Challenges encountered in contacting and interviewing DCWs included disconnected and wrong phone numbers and DCWs' failure to keep interview appointments, despite the fact that interviewers were willing to conduct interviews at odd hours and in any place and time convenient to the DCW.

Data Collection Procedures
We collected two types of data: (a) DCW-level data based on either in-person or telephone interviews with workers and (b) organizational-level data based on a mailed survey completed by the site liaison (usually a human resources director or the administrator). In cases where the organizational survey was incomplete, we contacted site liaisons to complete parts of the instrument over the phone. All 49 organizations in the study completed the survey.

Consent Procedures
Prior to starting the interview, we obtained informed consent from DCWs. The institutional review board of the research organization approved the consent procedures. The average amount of time taken to complete an interview was 50 min (SD = 34.58), and respondents received $20.

Measures
Outcome Variable
Job satisfaction was an overall measure of satisfaction with various aspects of a DCW job. The scale was originally developed for use with DCWs in nursing homes (Kiefer et al., 2005). Items are scored using a 4-point Likert scale, with higher scores indicating higher job satisfaction levels. We assessed the psychometrics of the scale in two steps. First, we dropped 2 of the original 18 items because of insufficient variance in responses (i.e., the criterion for exclusion was having less than 80%–20% response variance on an item). Second, we entered the remaining 16 items into a factor analysis using principal axis factoring and Varimax rotation. It revealed a one-factor solution. Thus, we used all 16 items (factor loadings.58–.80) to develop the scale (Cronbach's {alpha} =.94). (See the Appendix for the scale items.)


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Appendix Direct Care Worker Job Satisfaction Scale.

 
DCW Background Characteristics
We used single-item measures for the three DCW background characteristics: age, race/ethnicity (nonminority = 0; minority = 1), and marital status (unmarried = 0; married = 1). We considered including the number of months that the DCW had worked at the facility; however, we excluded it from the analyses because of its high correlation with the outcome variable.

DCW Personal Stressors
We used five measures to assess DCW personal stressors. One measure of perceived financial adequacy included six items covering whether the DCW had sufficient money to meet regular and unexpected obligations (Cronbach's {alpha} =.81). The second measure is an additive index of four items assessing worries about family while at work (Cronbach's {alpha} =.75). The health-related personal stressors included the Center for Epidemiologic Studies–Depression scale (CES-D; Radloff, 1977) and two single-item measures of perceived change in emotional health and physical health since becoming a DCW, scored as much worse (0), worse (1), about the same (2), better (3), or much better (4).

DCW Job-Related Stressors
We examined three components of job-related stress: (a) job design features, (b) training issues, and (c) pay and benefits. Job design features included a measure of scheduling changes (how often in the past 2 months the DCW had been asked to come in early, stay late, or work on a scheduled day off) and a measure of the frequency with which the DCW was permanently assigned to clients. Three single-item measures on training assessed perceived adequacy of job orientation to the facility, perceived adequacy of continuing education at the facility, and usefulness of having a mentor at the time of hire. With respect to pay and benefits, we included a one-item measure of being fairly compensated for the job along with four single-item measures of whether the organization provided the DCW with sick leave, paid holidays, a retirement or pension plan, and paid health insurance.

DCW Job-Related Support
We included three measures of job-related support in the analysis. We asked DCWs whether or not certain positive and negative reactions were elicited when the DCW interacted with other DCWs and with residents. Positive items focused on feelings of respect, affection, and reassurance, whereas negative items queried about feelings of anger, frustration, and guilt. However, we did not use positive items in the analysis because of a lack of variance. We created one additive index of total negative interactions on the job (regardless of with whom the negative interactions occurred) based on how many negative interactions were reported. In addition, DCWs reported the frequency of hearing racial or ethnic remarks made by residents or other staff members. We also used these two items on the frequency of racial or ethnic remarks by residents or other staff as independent measures of negative support.

Organizational Variables
Background Characteristics
We created several descriptive organizational variables for use in the analyses. The variables included type of LTC setting (assisted living/home health agency = 0; nursing home = 1); profit status (not for profit = 0; for profit = 1); percentage of minority residents/clients served; and percentage of resident/client services reimbursed through (a) Medicaid, (b) Medicare, or (c) private pay.

Organizational Issues
Based on the literature, we included selected measures of organizational issues. For example, the organizational survey included information on the extent to which the organization faced difficulties with turnover (4-point scale ranging from no difficulty to a great deal of difficulty) because of DCWs quitting or being fired, and the minimum starting hourly rate of pay for DCWs.

Data Analyses
Data analysis required several phases. In the first phase, we used a correlation matrix of all DCW independent variables in the model and the dependent variable to check for multicollinearity. Although an intercorrelation of.80 or higher is an example of a cutoff value at which to begin excluding predictors as being too similar (Stevens, 2002, p. 93), given the large number of predictors being considered, we used a more conservative cutoff of.50 for deciding to keep or exclude variables. The correlation of the outcome with the predictors retained in the analyses are included in Table 2.


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Table 2. Correlation Table of DCW Variables With DCW Job Satisfaction Score (N = 644).

 
In the second phase, we analyzed Research Question 1 by using a hierarchical multiple linear regression to determine the extent to which DCW reports of personal stressors, job-related stressors, and workplace support predicted DCW job satisfaction. We entered DCW predictor variables in four steps based on components of the LTC stress and support model: (a) background characteristics, (b) personal stressors, (c) job-related stressors, and (d) job-related support variables.

In the third phase, we addressed Research Question 2. In this phase, we used a two-level hierarchical linear model (HLM) to determine the extent to which organizational factors could predict DCW job satisfaction after we controlled for the effects of individual-level DCW predictors. We considered HLM the most appropriate statistical technique to use because study data were nested, in that a number of DCWs from each study site participated in the interviews.

We entered the significant predictor variables that we had identified earlier (Research Question 1) in Level 1 (individual- or DCW-level data) of the HLM analyses. In Level 2 (organizational-level data), we entered key organizational characteristics and management issues in the HLM analyses. We selected organizational variables based on their importance in the literature and used an iterative process to select only those that emerged as consistently significant. Due to the relatively small number of organizations (49), we entered no more than four organizational characteristics and management issues (using a ratio of 1 variable to 10 cases/organizations) at any one time in Level 2 of the HLM analyses.


    Results
 TOP
 Abstract
 Conceptual Model
 Methods
 Results
 Discussion
 References
 
Background and Context
Table 3 shows the DCW-level variables, scoring, ranges, means, and standard deviations. Approximately 6 out of 10 DCWs were minority, slightly more than one third were married, and the average age was 39 years. Eighty percent of all DCWs were state-test nursing assistants, indicating that even some home care aides and resident assistants had completed nurse assistant training. The majority of DCWs worked full time (77%), and the remaining 23% reported their work status as part time or "as needed." At the time of the interview, the mean length of time that DCWs had been with their current employers was 56 months (range = 0–84 months), or almost 5 years.


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Table 3. Ranges, Means, Standard Deviations, and Scoring for Direct Care Worker Study Variables (N = 644).

 
Worries about family while at work and particularly financial problems were widely reported by the DCWs. The average score for perceived job-related emotional health change since becoming a DCW was higher (2.26) than the mean score for physical health changes (1.98). Conversely, DCWs had relatively high CES-D scores: 26% scored at 16 or above, which is indicative of clinical levels of depressive symptoms.

On average, DCWs had been asked to make changes to their work schedule five times within the 2 months prior to their interview. Also, DCWs tended to perceive their job orientation and continuing education as fairly useful. Almost 75% of DCWs received paid holidays off, but only 49% received fully paid health insurance. On average, a DCW experienced one or two negative interactions with other staff or residents out of a possible three negative interactions. DCWs reported hearing racial/ethnic remarks more frequently from residents than from other staff.

Regarding organizational characteristics, Table 4 includes descriptive data on the three types of facilities. There was good representation of for-profit and nonprofit organizations (28 and 21, respectively). The organizations served, on average, 21% minority clients, although they ranged from serving no minority clients/residents to more than 90% minority clients/residents. Reimbursement sources also varied, with the average organization receiving about 40% of its reimbursement from Medicaid.


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Table 4. Organizations in the Study (N = 49).

 
DCW Individual-Level Predictors of Job Satisfaction
We entered 21 predictors in the hierarchical multiple linear regression (Research Question 1). The final model identified 12 significant predictors of DCW job satisfaction with an adjusted R2 of.51 (p <.001). We examined the tolerance and variance inflation factor (VIF) that focus on multicollinearity among the predictors and found that they were within acceptable limits (see Table 5). Background characteristics of DCWs were entered first and, although significant, they contributed little to the final outcome. The largest changes occurred with the inclusion of personal stressors (R2 change =.25, p ≤.001), the job-related stressors ( R2 change =.20, p ≤.001), and workplace support variables (R2 change =.05, p ≤.001). Thus, each step of the hierarchical regression analysis resulted in significant changes in the R2 value.


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Table 5. Multiple Linear Regression Results for Predicting Direct Care Worker Job Satisfaction (N = 556).

 
For the final model, in terms of background characteristics that were entered first, only race of the DCW predicted DCW job satisfaction, with nonminorities reporting higher levels of job satisfaction. Among the personal stressors that were entered next, three variables were significant predictors of DCW job satisfaction: physical health change since becoming a DCW, emotional health change, and CES-D score. DCWs who reported better physical and emotional health since working as a DCW and those who had lower depression scores were more likely to have higher job satisfaction.

Among the job-related stressors that were entered next, we identified significant predictors in relation to scheduling changes, training issues, and pay and benefits. Specifically, DCWs who perceived they had better on-the-job training in terms of the usefulness of continuing education and job orientation had higher job satisfaction. In terms of pay and benefits, DCWs who reported being fairly compensated for their job, having a retirement/pension plan, and having paid health insurance had higher job satisfaction.

With respect to workplace support variables that were entered last, racism and negative interactions were significant predictors of DCW job satisfaction. When DCWs reported hearing fewer racial or ethnic remarks from other staff and experienced fewer negative interactions at work, their job satisfaction was higher.

Organizational-Level Variables
We included significant DCW-level predictors that we identified in Phase 1 in Level 1 of the two-level HLM model. The y-intercept 0j) estimated from the Level 1 model represents the adjusted average job satisfaction scores of the jth LTC facility. Because the Level 1 predictors were group mean centered, the adjusted average represented the job satisfaction score for a typical DCW in a specific facility. We treated this index of job satisfaction as the outcome variable in Level 2 of the HLM analysis.

In Level 1 of the HLM analyses, all of the individual-level predictors identified in the multiple regression, except for race of DCW, emerged as significant predictors of the adjusted average job satisfaction level in a facility. With respect to organizational factors identified in Level 2 of the HLM analyses, the following were significant: type of LTC facility (i.e., nursing home vs home health agency and assisted living facility), difficulty with turnover of DCWs, and minimum hourly rate of pay for DCWs (see Table 6). Nursing homes had lower adjusted average job satisfaction scores compared to home health agencies and assisted living facilities. Greater difficulty with DCWs quitting or being fired had a negative relationship, whereas higher rates of starting wages had a positive relationship with the LTC facility's adjusted average DCW job satisfaction score. The percentage of minority clients served was not a significant predictor of the adjusted average DCW job satisfaction score.


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Table 6. Hierarchical Linear Modeling Results on Organizational Factors Predicting DCW Job Satisfaction (N = 49 Organizations).

 

    Discussion
 TOP
 Abstract
 Conceptual Model
 Methods
 Results
 Discussion
 References
 
The LTC stress and support model, tested with data from both DCWs and organizations, proved to be a useful tool to explain DCW job satisfaction. Specific components of the model had more explanatory power than others. For example, Step 1 of the multiple regression analysis identified only one background characteristic (i.e., nonminority race) as a significant predictor of DCW job satisfaction. However, because race was not significant in the HLM analyses when we entered the organizational-level variables, it suggests that race and other DCW background characteristics were not as important in predicting job satisfaction as were other variables. In fact, the most powerful predictors of DCW-level job dissatisfaction were the personal and job-related stressors.

Among the personal stressors, physical and emotional health changes since working as a DCW and depression were significant. From a practice perspective, LTC organizations need to be sensitive to perceived changes in the health of DCWs. Offering workplace health screening, employee assistance, and health promotion programs may be efficacious in addressing DCWs' personal stress in order to enhance their job satisfaction.

In addition to personal stressors, a variety of job-related stressors were significant predictors of job dissatisfaction. They fell into the following categories: issues related to scheduling changes, training, and pay and benefits. Organizations can alleviate some job-related stress by offering better job orientation and continuing education programs. In fact, DCW-related findings from our study indicate that DCWs want better training in specific content areas and in the methods for providing training (Menne, Ejaz, Noelker, & Jones, 2007).

Although LTC organizations are not always amenable to offering better pay and benefits, it is clear that feeling fairly compensated for the job and receiving key benefits are significantly related to DCW job satisfaction. Of all of the benefits included in our study, two were significant: health insurance and a retirement/pension plan. Other benefits such as paid holidays and paid sick leave did not emerge as significant. Because studies have suggested that job satisfaction is related to turnover (Castle et al., 2006), further research needs to be conducted examining the business case for offering better wages and benefits in relation to the cost of turnover.

In addition to addressing sources of job-related stress, organizations can also focus on improving communication patterns and relationships in the workplace. Our findings suggest that perceptions of racism by other staff and negative interactions with staff and residents are likely to generate greater job dissatisfaction. With respect to racism, our findings support those of other studies in that DCWs were more likely to condone racist remarks from residents and clients because they believed that the elders were too impaired to be maliciously racist (Berdes & Eckert, 2001). Hence, racism from staff was a significant predictor of job satisfaction but racism from residents was not, even though 70% of the DCWs in our sample had heard residents and clients make racist remarks. Thus, having sensitivity training on racial and cultural differences; promoting effective communication between residents, families, and staff via newsletters, brochures, and discussion groups; and having a no-tolerance policy on discrimination are likely to enhance workplace support.

In terms of differences between organizations, it is interesting to find that nursing homes had lower DCW job satisfaction levels compared to home health agencies and assisted living facilities. Additionally, organizations providing a higher minimum starting rate of pay for DCWs and those not struggling with turnover issues were more likely to have higher levels of DCW job satisfaction. In a low-income population of working women, the issue of pay is likely to be linked to the alarming rates of DCW turnover in the industry (Harris-Kojetin et al., 2004; U.S. Department of Health and Human Services, 2003, 2004; U.S. General Accounting Office, 2001).

The limitations of this study include the following: (a) the inclusion of sites from a restricted geographical area (five-county area in Ohio), (b) the inability to obtain a random sample of DCWs due to the challenges involved in contacting and completing interviews with DCWs who volunteered to participate, and (c) the lack of longitudinal data to examine cause and effect between variables in the model. Thus, the findings from this study need to be viewed with caution, particularly those regarding the differences between nursing homes and other LTC organizations. We could not conduct a more in-depth examination of the differences between the three types of LTC settings in our sample because there were fewer home health agencies and assisted living facilities than nursing homes due to the proportionate sampling techniques used in the study. To examine the effect of organizational variables while controlling for DCW individual-level factors by type of LTC setting, HLM would require a minimum of 30 organizations for each of the three types of settings. Thus, future studies using larger samples of organizations are required to effectively address differences by type of setting.

Similarly, although DCWs in Ohio are likely to have characteristics similar to other samples of DCWs in terms of gender and income level, the study questions and the conceptual model need to be tested with larger, more generalizable samples.

One of the major strengths of the study lies in the use of a conceptual model that supported using two levels of data (i.e., DCW and organizational levels) to predict job satisfaction. Other strengths include the preliminary examination of three types of LTC settings and the large sample of 644 DCWs who completed in-depth interviews. Future research with larger, more representative samples and a longitudinal design can lead to further refinements in the LTC stress and support model for examining the relationship between job satisfaction and actual turnover among DCWs.


    Footnotes
 
Supported by the Better Jobs Better Care initiative that was funded by the Robert Wood Johnson Foundation and the Atlantic Philanthropies and managed by the Institute for the Future of Aging Services, American Association of Homes and Services for the Aging. We would like to thank Kathleen Fox, project coordinator; Dorothy Schur and Julie Rentsch, research analysts; the participating sites and their liaisons; the interviewers; and the direct care worker respondents. Back

1 Margaret Blenkner Research Institute, Benjamin Rose Institute, Cleveland, OH. Back

2 College of Education and Human Services, Cleveland State University, OH. Back


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All GSA journals Journals of Gerontology Series A: Biological Sciences and Medical Sciences Journals of Gerontology Series B: Psychological Sciences and Social Sciences