Qual Life Res DOI 10.1007/s11136-015-0989-4
Nursing home resident quality of life: testing for measurement equivalence across resident, family, and staff perspectives Judith Godin1,2 • Janice Keefe3 • E. Kevin Kelloway4 • John P. Hirdes5
Accepted: 7 April 2015 Ó Springer International Publishing Switzerland 2015
Abstract Purpose This study explores the factor structure of the interRAI self-report nursing home quality of life survey and develops a measure that will allow researchers to compare predictors of quality of life (QOL) across resident, family, and staff perspectives. Methods Nursing home residents (N = 319), family members (N = 397), and staff (N = 862) were surveyed about their perceptions of resident QOL. Exploratory factor analyses were conducted on a random half of the staff data. Subsequently, confirmatory factor analysis was used to test for measurement equivalence across the three perspectives. Results The final model had a four-factor structure (i.e., care and support, food, autonomy, and activities) across all three perspectives. Each factor had at least two items that
were equivalent across all three perspectives, which suggests at least partial measurement equivalence. Conclusion The finding of partial measurement equivalence acknowledges there are important differences between perspectives and provides a tool that researchers can use to compare predictors of QOL, but not levels of agreement across perspectives. Targeting these four aspects is likely to have the additional benefit of improving family and staff perceptions of resident QOL in addition to the resident’s own QOL. Keywords Quality of life Measurement equivalence Long-term care Nursing home residents Family perceptions Staff perceptions InterRAI
Introduction
& Judith Godin [email protected] 1
Nova Scotia Centre on Aging, Mount Saint Vincent University, 166 Bedford Highway, Halifax, NS B3M 2J6, Canada
2
Geriatric Medicine Research, Nova Scotia Health Authority, 5955 Veteran’s Memorial Lane, Halifax, NS B3H 2E1, Canada
3
Department of Family Studies and Gerontology, Nova Scotia Centre on Aging, Mount Saint Vincent University, 166 Bedford Highway, Halifax, NS B3M 2J6, Canada
4
Department of Psychology, Saint Mary’s University, 923 Robie Street, Halifax, NS B3H 3C3, Canada
5
School of Public Health and Health Systems, University of Waterloo, 200 University Avenue West, Waterloo, ON N2L 3G1, Canada
Quality of life (QOL) is a comprehensive construct that has been defined differently across contexts [1]. For example, some QOL assessments focus on specific diseases (e.g., quality of life—Alzheimer’s disease), while others focus on health and functional status (e.g., 36-item short-form survey) [2, 3]. Others employ a multidimensional assessment, covering a broader range of QOL domains [4]. QOL is a subjective concept; it encompasses much more than health and functional status [5]. This subjectiveness makes developing a QOL assessment tool that can be used to examine different perspectives (e.g., nursing home resident, family, and staff) very challenging. Staff and family members’ perceptions may differ from the residents’ own perceptions, but may still be of interest to researchers, policy makers, and administrators. In an ideal nursing home environment, residents, family, and staff all perceive resident QOL to be good.
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Different perspectives of resident quality of life
Purpose
Some researchers have argued that the resident perspective is the only appropriate assessment of QOL [6, 7]. Researchers have reported that residents are capable of answering QOL questionnaires even in cases where MiniMental State Exam scores are as low as three [3, 8]. An ongoing debate exists as to whether other people’s perceptions should be taken into consideration when assessing resident QOL [9]. Although the residents’ own perspective may be the most salient and important assessment of their QOL, understanding the perceptions of family members and staff is also important. Caregiver burden may increase if family members believe their relatives are experiencing poor QOL, regardless of the perceptions of the resident [13, 15]. Family members have the potential to advocate for residents. They may be able to influence policy change and draw attention to quality issues, such as housekeeping [16]. Staff perceptions of residents’ QOL may influence how they deliver care and allocate resources [17, 18]. Both family and staff make decisions for residents regarding care and daily activities. These decisions are likely based on a desire to increase or maintain the resident’s QOL. Although another person’s perceptions should never be used as a substitute or proxy for a resident’s own report of QOL, information from family and staff could add context and paint a more complete picture of QOL within an organization. In response to the argument that residents report good QOL because they have acclimated to their environment, Kane and colleagues argue that this acclimation can be seen as successful coping—a key component of QOL [9]. Still, others’ perceptions could be used to expand the evidence regarding resident QOL. For example, a resident may report satisfaction with food because he or she has learned to cope with the new environment. Introducing a favorite meal into the menu based on a family member’s suggestion may improve resident satisfaction and mood. Most of the research examining different perspectives of QOL has focused on proxy reports. However, such reports can differ substantially from family and staff perceptions about another individual’s QOL. Specifically, when using proxies, family and staff are asked to respond to the questions as they feel the resident would respond [9]. While these perspectives of QOL are valid and can provide additional information [10], they should not be equated with the resident’s views by default. As such, in the current research, we asked nursing home staff and family members about their perceptions of resident QOL because we viewed each perspective as distinct and important.
Our research develops a measure of nursing home resident QOL that can be used to assess staff and family perceptions in addition to the residents’ own perceptions of QOL. Although the actual distributions of ratings on summary scales may be different across the three perspectives, this new measure needs to tap similar domains of QOL in order to allow meaningful comparisons across perspectives regarding predictors of QOL. The importance of family and staff perceptions of resident QOL has received only limited attention in the literature. To the best of our knowledge, no studies have reported on a measure of QOL that has been explicitly assessed for measurement equivalence. Our research addresses this gap in the literature by forwarding a new tool that can be used to assess resident, family, and staff perceptions of resident QOL.
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Methods We recruited resident, family, and staff participants from 23 publicly funded nursing homes across Nova Scotia, Canada. Subsequently, the invitation to participate was extended to staff from other nursing homes in Nova Scotia. Each of these participant groups completed a survey that assessed perceptions of resident QOL. Ethics review boards of all participating universities and, where appropriate, participating nursing homes and regional health authorities approved the research. All responses were kept confidential and were identified only at the facility level, with the exception that respondents who indicated they would be interested in participating in follow-up research provided their names and contact information. After data collection, this identifying information was separated from the survey data. Procedure We sent recruitment letters to potential resident, family member, and staff participants. The letters contained background information on the project, identified when onsite information sessions would take place, and outlined how to take part in the survey or find out more information about the project. The introduction to the survey informed family and staff participants that we were interested in their perspective as a family member or staff (e.g., this survey asks you to share your perspective as a staff member of a nursing home in Nova Scotia). This statement was included to ensure that responses
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to the survey reflected their own perceptions. The residents, family members, and staff were not linked as triads. Residents of any age who lived in the nursing home for at least 1 month could participate in the study. Recruitment letters were hand delivered to all residents regardless of cognitive ability. If staff felt the letter might cause distress, they could choose not to give the letter to the resident. Residents who participated in the survey completed an informed consent procedure with a research assistant. The research assistant asked the resident questions about the consent form to ascertain that the resident understood the consent information. If the questions were answered appropriately, the research assistant would proceed with the informed consent and survey. If the resident could not answer any of the questions, the research assistant thanked the resident for his or her interest and closed the survey. Consent was audio recorded for situations in which written consent was not possible. In cases where neither written nor audio consent was possible, the resident indicated consent in their usual manner of communicating (e.g., head nod, pointing to a ‘yes’ icon on a board or typing). The first page of the family and staff surveys provided the information necessary for informed consent. Survey completion and return implied consent. Family and staff were offered either paper or online versions of the survey. Because we do not have accurate numbers regarding how many people were invited to respond, we do not have exact response rates. Instead, participation rates were approximated by comparing the number of responses to the total number of beds and the total number of staff at participating facilities. Based on this, 13 % of residents, 16 % of family members, and 11 % of staff participated in the survey. Participants
had lived in their current facility for more than a year (73 %). Three-quarters of these residents were female (77 %) and between the ages of 75 and 94 (79 %). Staff Of the 862 staff who responded to the survey, most were female (91 %). Ages ranged from 16 to 71, with an average age of 43 (SD = 11.7). Thirty-eight percent were continuing care assistants (CCAs), 18 % were registered or licensed practical nurses, 3 % were other care staff, such as occupational therapists, 21 % were support staff (e.g., housekeeping and dietary), 13 % were in administration, and 7 % did not report their job title. Two-thirds of staff respondents were full time (66 %), one-quarter were part time (26 %), and the remainder were casual.
Measures Resident quality of life The interRAI self-report survey on nursing home quality of life was used in the resident, family, and staff surveys [11]. The resident version has been used mainly with residents who scored from 0 (intact) to 3 (moderate impairment) on the Cognitive Performance Scale [12]. The survey was designed to be administered to nursing home residents [11], and we adapted the survey for family and staff perspectives. We altered the items slightly for each perspective (e.g., I feel safe around those who provide me with support and care, My family member is safe around those who provide him/her with support and care, and Residents are safe around those who provide their support and care). Response options were on a scale of 0 ‘never’ to 4 ‘always.’
Nursing home residents Additional variables Of the 319 residents who responded to the survey, most were female (73 %) and between the ages of 75 and 94 (62 %). Seven percent were 95 years of age or older, and 30 % were under the age of 74. A large number of residents were in their current nursing home for longer than 2 years (46 %). Family members Of the 397 family members who responded to the survey, most were female (78 %) and between the ages of 55 and 74 (68 %). Three-quarters were married (79 %). Over half were retired (55 %), and a quarter worked full time (27 %). Over half (58 %) were the primary caregivers of the resident prior to admission. Two-thirds (64 %) of family members were the child of the nursing home resident. Most of the residents that the family members were referencing
Three variables not included in the main analyses were used to impute missing data. Two single-item measures were used: ‘How would you describe your overall quality of life?’ and ‘Given your health status today, how would you describe your overall experience of living in this nursing home?’ The wording of these items was adapted to reflect the appropriate perspective in the family and staff surveys. There were five response options for these questions that ranged from ‘very poor’ to ‘very good,’ with a middle category of ‘neutral.’ Each participating nursing home belonged to one of three models of care based on characteristics of its physical design and staffing. Specifically, participants were from a traditional facility (i.e., traditional physical design, CCAs provide care needs only), a new augmented facility (i.e.,
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neighborhood design, CCAs provide care and do some other tasks, such as housekeeping), or a new full-scope facility (i.e., neighborhood design, CCAs responsible for care, housekeeping, and dietary tasks). Statistical procedures Missing data Missing data on the QOL items ranged from 4.2 to 17.9 % (M = 6.3 %, SD = 2.06 %) in the staff data, 1.5 to 50.1 % (M = 17.3 %, SD = 11.01 %) in the family data, and 1.3 to 21.3 % (M = 6.8 %, SD = 4.91 %) in the resident data. For the item that was missing 50.1 % of the data (i.e., residents/staff asked before using my family member’s things), 142 (35.8 %) indicated that they did not know and 47 (11.8 %) said that the question was not applicable. The remaining 10 family members skipped the question. The most widely used method of handling missing data is listwise deletion; however, listwise deletion is known to produce biased and inaccurate results [13, 14]. Multiple imputation (MI) provides less biased and more accurate estimates [15–18]; thus, with the NORM software [19], we used multiple imputation to address missing data. All variables included in the main analyses must be used in the imputation model, and including additional variables is beneficial [15, 16, 20]. We included in the 49 interRAI QOL items, perspective, the single-item measure of QOL and nursing home experience, and model of care in the imputation model. The exploratory and the initial confirmatory factor analyses were conducted on a single imputed dataset [18]. The final model was tested on five imputed datasets, and the results were pooled according to the guidelines outlined by Enders [15]. Exploratory factor analyses (EFA) We were interested in conducting both exploratory and confirmatory analyses; however, conducting these analyses on the same data is inappropriate. The staff dataset was the only dataset large enough to randomly split into two halves: one for exploratory analyses and one for confirmatory analyses (n = 432 and n = 430, respectively). This large sample size provided the most reliable exploratory analyses. The EFAs were conducted using IBM SPSS Statistics 20. Prior to conducting each EFA, we conducted Velicer’s minimum average partial and parallel analyses to determine the number of factors [21]. Generalized least squares was used to extract the factors with direct oblimin rotation. In order to be retained, items had to have a factor loading of six or higher in either the pattern or structure matrix, had to load onto the same factor in both matrices, and could not crossload onto multiple factors in the pattern matrix.
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Confirmatory factor analysis All confirmatory factor analyses were conducted using EQS version 6.1 for Windows. In order to optimize the model for each group, we first tested the hypothesized model (see Fig. 1) separately with each perspective (i.e., staff, family, and resident) [22, 23]. Due to high normalized Mardia’s coefficients (NMC = 21.70, 49.90, and 40.63 for staff, family, and resident perspectives, respectively) [23], we reported robust fit statistics and used robust standard errors in the calculation of significance tests. A comparative fit index (*CFI) [.90, a root mean squared error of approximation (*RMSEA) \.08, and a standardized root mean square residual (*SRMR) \.10 indicated good fitting models. We adjusted the hypothesized model for each perspective based on the results of Lagrange’s multiplier tests (LMTest), standardized residuals, and theoretical considerations. For example, the item ‘Residents can eat when they want’ was removed from the staff perspective because the LMTests and standardized residuals indicated this item was contributing to poor fit and it made intuitive sense that staff may not view this item as important if they do not receive any complaints from residents. DSantora–Bentler (S–B)v2 was used to assess each model modification [24]. Cheung and Rensvold’s recommendation of a change in CFI of more than .01 was used to assess changes in fit due to adding or removing constraints [25].
Results Exploratory factor analysis on staff data We conducted a series of EFAs on a random half of the staff data. For each EFA, items that did not meet the criteria specified above were removed and a subsequent EFA was conducted. These procedures were repeated until all items met the criteria for retention. The excluded items are listed in Table 1. Minimum average partial and parallel analyses on the final 29 items indicated four components were present. Care and support, satisfaction with food, autonomy, and activities emerged in the final EFA and accounted for 54 % of the variance. Confirmatory factor analysis Staff perspective All estimated parameters were significant at the p \ .05 level; however, fit statistics were less than ideal (see Table 2). We made three sequential modifications to the
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Resident Quality of Life
privacy is respected
privacy of personal information
feeling safe among staff
able to be alone
getting needed help
can visit in private
get needed services
eat when want
staff attention
free to go outside
Care and Support
expressing opinion
Autonomy decision going to bed
decision spending time
treated with dignity by staff
free to go when/where they want
likes are respected
Food
staff responsiveness
Activities
control access to room
can have bath or shower
timely delivery of services
living life the way they want
like the food
enjoy mealtimes
favourite foods
variety in meals
enjoyable weekend activities
keep mentally active
off unit activities
meaningful activities
Fig. 1 Hypothesized model for confirmatory factor analyses on staff, family, and resident data
hypothesized model: ‘When residents have company, they can visit in private,’ ‘Residents’ personal information is kept private,’ and ‘Residents can eat when they want’ were deleted. Family perspective The fit statistics were less than ideal for the hypothesized model (see Table 3), but all estimated parameters were significant at p \ .05. We made two sequential modifications to the hypothesized model: ‘My family member’s personal information is kept private’ and ‘When my family member has company, he/she can visit in private’ were deleted.
Resident perspective All the estimated parameters were significant at p \ .05; however, the fit statistics were less than ideal (see Table 4). We made two sequential modifications: ‘When I have company, I can visit in private’ and ‘I can be alone when I wish’ were deleted from the model. Measurement invariance across the three perspectives When the model was tested simultaneously across the three perspectives (i.e., configural equivalence), the model fit the data reasonable well (see Table 5). The *CFI dropped by
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Qual Life Res Table 1 Items excluded based on the EFA results
Original QOL domain
Item
Safety/security
Possessions are safe Safe when alone Ask to use my things
Comfort
Recommend site to others Feels like home Bothered by noise
Autonomy
Decide how to spend money
Respect
Careful what I say around staff
Responsive staff
Act on suggestions
Staff–resident bonding
Know the story of my life Time to have a conversation with me Know how to meet my needs Staff member a friend Open and honest
Activities Personal relationships
Religious activities that have meaning to me Another resident is my friend People want to do things with me Ask for my help Important role in people’s lives Opportunities for romance
Table 2 Goodness of fit statistics for the staff perspective
v2
dfs
S–Bv2
*AIC
SRMR
*CFI
*RMSEA (90 % CI)
1203.04
457.04
.073
.843
.072 (.067, .076)
.871
.066 (.061, .071)
.066
.886
.064 (.058, .069)
.063
.900
.061(.055, .066)
Hypothesized model 1356.11
373
Removal of ‘When residents have company, they can visit in private’ DS–Bv2 = 210.20, p \ .001 1122.11
346
994.81
302.81
.067
Removal of ‘Residents’ personal information is kept private’ DS–Bv2 = 112.67, p \ .001 971.10 320 874.30 234.30 Removal of ‘Residents can eat when they want’ DS–Bv2 = 116.00, p \ .001 842.81
295
758.52
168.53
2
Values for DS–B v were calculated using the procedures recommended by [26]
Table 3 Goodness of fit statistics for the family perspective
v2
dfs
Hypothesized model 1285.12 373
S–Bv2
*AIC
SRMR
*CFI
*RMSEA (90 % CI)
1049.93
303.93
.069
.862
.068 (.063, .072)
.880
.066 (.061, .071)
Removal of ‘My family member’s information is kept private’ DS–Bv2 = 101.93, p \ .001 1122.16
346
939.21
247.22
.068
Removal of ‘When my family member has company, he/she can visit in private’ DS–Bv2 = 129.59, p \ .001 943.58
320 2
799.73
159.73
.066
.902
Values for DS–Bv were calculated using the procedures recommended by [26]
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.062 (.056, .067)
Qual Life Res Table 4 Goodness of fit statistics for the resident perspective
v2
S–Bv2
dfs
*AIC
SRMR
*CFI
*RMSEA (90 % CI)
.071
.862
.051 (.045, .057)
.066
.893
.045 (.038, .052)
.065
.905
.044(.036, .050)
Hypothesized model 852.54
373
679.56
-66.44
Removal of ‘When I have company, I can visit in private’ DS–Bv2 = 110.11, p \ .001 717.92
346
571.12
-120.88
Removal of ‘I can be alone when I wish’ DS–Bv2 = 58.27, p \ .001 648.65
320
513.75
-126.252
2
Values for DS–Bv were calculated using the procedures recommended by [26]
.009 when we constrained the first-order factor loadings to be equivalent across perspectives. A drop of .009 is close to the suggested cutoff, and a *CFI of .89 indicates a poor fitting model. Further, LMTests for releasing constraints indicated that there were 10 factor loadings that were not equivalent across perspectives. When appropriate constraints of the first-order factor loadings were lifted, model fit was similar to the fit for configural invariance (D*CFI = .002), indicating partial measurement equivalence. Partial measurement equivalence is sufficient to proceed to the test of structural equivalence of the higher-order factor [26]. Care and support, food, and autonomy exceeded the requirements, and activities met the requirements for partial measurement equivalence (i.e., at least two equivalent items per factor [26]). Items that were not equivalent across perspectives were retained as these items likely contain important information regarding the differences across perspectives [27]. Structural invariance across the three perspectives When the second-order factor loadings were constrained to be equal across the three perspectives, there was very little change in fit from either the previous model D*CFI = .003 or the configural equivalence model (D*CFI = .005; see Table 5), indicating that the structure of the second-order
Table 5 Goodness of fit statistics for testing equivalence across the three perspectives
v2
dfs
factor is equivalent across groups. Factor loadings for the final model and internal reliabilities for the factors and resident QOL can be given in Tables 6 and 7, respectively.
Discussion We found partial measurement equivalence across resident, family, and staff perspectives, which enables comparisons of predictors of resident QOL across perspectives. Not surprisingly, this finding also suggests that nursing home residents, family, and staff have somewhat different conceptualizations of resident QOL. Each perspective places varying levels of importance on different aspects of QOL [28]. With the level of measurement equivalence demonstrated in the current research, assessing agreement between groups is not appropriate: The varying levels of importance the perspectives place on individual items is likely to bias results [29]. Further, to assess agreement across perspectives, we would have to test for a stronger level of equivalence. Partial measurement equivalence does allow for the examination of relations of other variables to QOL within each of these perspectives [23, 29]. The higher-order structure was equivalent across all three perspectives, but there were some discrepancies with the individual items. Of the 29 retained items, only one item, If I
S–Bv2
*AIC
SRMR
*CFI
*RMSEA (90 % CI)
2052.31
182.30
.065
.90
.06 (.05, .06)
.89
.06 (.05, .06)
.90
.06 (.05, .06)
Configural equivalence 2435.04
935
Measurement equivalence (first-order factor loadings constrained) 2618.70
979
2199.09
241.087
.077
Measurement equivalence (some first-order constraints lifted) 2507.76
968
2112.45
176.45
.069
Final model—structural equivalence (constraints added to second-order factor loadings) 2558.59 976 2154.02 202.02 .118 Final model—pooled fit statistics from five imputations
.90
.06 (.05, .06)
2568.17
.90
.06(.05, .06)
976
2464.26
212.26
.121
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Qual Life Res Table 6 Factor loadings for final model Factor or item
Staff
Family
Resident
Care and support
0.42
0.42
0.42
Privacy is respected
0.81
0.81
0.61
Privacy of personal information
Excluded
Excluded
0.46
Feeling safe among staff
0.62
0.62
0.62
Getting needed help
1.04
1.29
0.88
Get needed services
0.93
0.93
0.77
Staff attention
1.05
1.05
1.05
Expressing opinion
1.17
1.17
0.92
Treated with dignity by staff
1.00
1.00
1.00
Likes are respected
1.16
1.16
1.16
Staff responsiveness Timely delivery of services
1.14 1.14
1.14 1.14
1.14 1.14
Living life the way they want
1.21
1.21
1.21
Food
0.37
0.37
0.37
Like the food
1.00
1.00
1.00
Enjoy mealtimes
0.97
0.97
0.97
Favorite foods
1.13
1.13
1.13
Variety in meals
1.34
1.04
1.04
Autonomy
0.63
0.63
0.63
Able to be alone
0.60
0.60
Excluded
Eat when want
Excluded
0.88
0.88
Free to go outside
1.00
1.12
1.00
Decision going to bed
1.00
1.00
1.00
Decision spending time
0.94
0.70
0.70
Free to go when/where they want
1.00
1.00
1.35
Control access to room
1.01
1.01
1.01
Can have bath or shower Activities
1.07 0.65
1.07 0.65
1.07 0.65
Enjoyable weekend activities
0.87
0.87
0.65
Keep mentally active
0.95
0.95
0.52
Off unit activities
0.91
0.91
0.91
Meaningful activities
1.00
1.00
1.00
need help right away, I can get it, had a different factor loading for each perspective. The four-factor structure of care and support, food, autonomy, and activities loading onto resident QOL was equivalent across the three perspectives, and over 50 % of the items were equivalent across all three perspectives. For pairs of perspectives, 55 % were equivalent between staff and residents, 62 % were equivalent Table 7 Cronbach’s alpha for subscales and overall quality of life
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Factor
between family and residents, and 76 % were equivalent between staff and family perspectives. These differences, however, do not diminish the ability to make comparison of predictors of QOL across perspectives [22, 23]. Noticeably, the family and staff perspectives were more similar to each other than to the resident perspective as has been found in other research [30]. When assessing someone else’s QOL, raters may report on how they would feel if they were in the residents’ situation and place less emphasis on how residents report they feel. This type of perspective taking may account for some of the differences between residents’ perspectives and family and staff perspectives. Past research provides support for the four factors (i.e., care and support, food, autonomy, and activities). Researchers have identified autonomy and meaningful activities as key components of QOL [28, 31, 32]. Security and safety [9], comfort [28, 31], personal attention [4], and care [32] have been identified as aspects of QOL and are reflected in the care and support factor. Further, care and support and autonomy are considered important aspects of person-centered care [33]. Dementia patients, family members, and staff all identified meaningful activities as an important component of person-centered care [34]. Food has been found to be a component of multidimensional assessments of QOL [4], and researchers have found that food enjoyment was associated with overall satisfaction with the nursing home [35]. The interRAI self-report survey of nursing home quality of life [11], which was used in this research, does not include health or functional status because these are addressed by the clinical assessment instruments such as the interRAI long-term care facility assessment [36]. When asked to describe nursing home resident QOL, focus groups of residents, family, and staff all emphasized more subjective components, such as dignity and autonomy, and placed little focus on more ‘objective components,’ such as health and functional status [37]. Residents’ perceptions of their own QOL are important and cannot be replaced by family or staff perceptions when assessing policy and interventions designed to improve resident QOL. Understanding family and staff perceptions, however, still provides a valuable contribution to research into long-term care outcomes. Family members can
Staff (first half/second half)
Family
Resident
Care and support
.91/.90
.93
.87
Food
.85/.84
.87
.83
Autonomy
.86/.85
.88
.75
Activities
.85/.81
.83
.66
Overall quality of life
.78/.75
.82
.69
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experience increased distress if they believe that their loved one is experiencing poor QOL. Family members advocate for residents through their communication with administration and staff at long-term care facilities. Staff members decide how to interact with and care for residents based on their own perceptions or residents’ QOL. Understanding others’ perspectives of resident QOL is important even if the perspectives cannot be used to directly assess resident QOL. Limitations We found a four-factor structure of QOL using the staff data. Our hypothesized model may have differed had we conducted the EFA on a different perspective. We started from the staff perspective because the staff sample was large enough to randomly split the data into two halves. Splitting the data enabled us to conduct the CFAs on different data from the EFAs. Even though the four-factor structure was developed from the staff data, the structure was equivalent across the three perspectives. Fifteen of the individual items were equivalent across all perspectives. The factors that emerged in the current study may not represent the breadth of residents’ experience of QOL; however, these factors do represent aspects of QOL that can be assessed across the three perspectives. Because residents needed to have sufficient cognitive ability to provide consent, the findings from the resident perspective may not generalize to residents with severe cognitive impairment. Further, the truncated range of cognitive impairment in the resident perspective meant there was variability between perspectives in the ranges of cognitive ability. Although residents with severe cognitive impairment could not participate, family member participants were able to respond even if the resident had severe cognitive impairment and staff members were answering the questions based on ‘residents’ in general. These differences between perspectives may have contributed to measurement inequivalence. A substantial amount of data were missing and handled through multiple imputation. Although a certain amount of uncertainty is inherent when conducting analyses with any amount of missing data, multiple imputation is a state-ofthe-art technique that produces accurate estimates and takes into consideration this uncertainty [15, 16, 18].
Conclusion Nursing home staff, family members, and residents shared some similarities in their conceptualizations of resident QOL. The four factors that emerged (i.e., care and support, autonomy, food, and activities) can be targeted by nursing homes aiming to improve resident QOL. Nursing home
residents often have poor health and some cognitive impairment, and these elements of their lives are extremely difficult to change; thus, focusing on other aspects is important for improving resident QOL. Targeting these four aspects is likely to have the additional benefit of improving family and staff perceptions of resident QOL. This paper presents a conceptualization of QOL that is similar across the perspectives, thus enabling a greater understanding of how predictors of QOL vary across perspectives. The finding of partial measurement equivalence acknowledges differences across perspectives and at the same time provides a tool that researchers can use to compare predictors of QOL. Although future research is needed to further validate the tool for each of the perspectives, having a tool that is at least partially equivalent enables researchers to better understand what predicts different perceptions of QOL. Acknowledgments This work was supported by a Partnerships for Health System Improvement Grant funded by the Canadian Institutes of Health Research (FRN # 114120) and the Nova Scotia Health Research Foundation (Matching-2011-7173) and a Nova Scotia Health Research Foundation 2013 Scotia Support Grant (PSO-Research Programs-2013-9039). John Hirdes was supported through the Ontario Home Care Research and Knowledge Exchange Chair funded by the Ontario Ministry of Health and Long-Term Care. Conflict of interest
None.
Ethical standard Ethics review boards of all participating universities and, where appropriate, participating nursing homes and regional health authorities approved the research; therefore, this research has been performed in accordance with the ethical standards laid down in the 1964 Declaration of Helsinki and its later amendments.
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24. Satorra, A., & Bentler, P. M. (2001). A scaled difference Chisquare test statistic for moment structure analysis. Psychometrika, 66(4), 507–514. 25. Cheung, G. W., & Rensvold, R. B. (2002). Evaluating goodnessof-fit indexes for testing measurement invariance. Structural Equation Modeling: A Multidisciplinary Journal, 9(2), 233–255. doi:10.1207/S15328007SEM0902_5. 26. Byrne, B. M., Shavelson, R. J., & Muthen, B. (1989). Testing for the equivalence of factor covariance and mean structures: The issue of partial measurement in variance. Psychological Bulletin, 105(3), 456–466. 27. Cheung, G. W., & Rensvold, R. B. (1998). Cross-cultural comparisons using non-invariant measurement items. Applied Behavioral Science Review, 6(1), 93–110. doi:10.1016/S10688595(99)80006-3. 28. Kane, R. L., Rockwood, T., Hyer, K., Desjardins, K., Brassard, A., Gessert, C., & Kane, R. A. (2005). Rating the importance of nursing home residents’ quality of life. Journal of the American Geriatrics Society, 53(12), 2076–2082. doi:10.1111/j.1532-5415. 2005.00493.x. 29. Borsboom, D. (2006). When does measurement invariance matter? Medical Care, 44(11 Suppl 3), S176–S181. doi:10.1097/01. mlr.0000245143.08679.cc. 30. Beer, C., Flicker, L., Horner, B., Bretland, N., Scherer, S., Lautenschlager, N. T., et al. (2010). Factors associated with self and informant ratings of the quality of life of people with dementia living in care facilities: A cross sectional study. PLoS One, 5(12), e15621. doi:10.1371/journal.pone.0015621. 31. Gerritsen, D. L., Steverink, N., Ooms, M. E., & Ribbe, M. W. (2004). Finding a useful conceptual basis for enhancing the quality of life of nursing home residents. Quality of Life Research: An International Journal of Quality of Life Aspects of Treatment, Care and Rehabilitation, 13(3), 611–624. 32. Ball, M. M., Whittington, F. J., Perkins, M. M., Patterson, V. L., Hollingsworth, C., King, S. V., & Combs, B. L. (2000). Quality of life in assisted living facilities: Viewpoints of residents. Journal of Applied Gerontology, 19(3), 304–325. doi:10.1177/ 073346480001900304. 33. White, D. L., Newton-Curtis, L., & Lyons, K. S. (2008). Development and initial testing of a measure of person-directed care. The Gerontologist, 48(Suppl 1), 114–23. Retrieved from http:// www.ncbi.nlm.nih.gov/pubmed/18694992. 34. Edvardsson, D., Fetherstonhaugh, D., & Nay, R. (2010). Promoting a continuation of self and normality: Person-centred care as described by people with dementia, their family members and aged care staff. Journal of Clinical Nursing, 19(17–18), 2611–2618. doi:10.1111/j.1365-2702.2009.03143.x. 35. Burack, O. R., Weiner, A. S., Reinhardt, J. P., & Annunziato, R. A. (2012). What matters most to nursing home elders: Quality of life in the nursing home. Journal of the American Medical Directors Association, 13(1), 48–53. doi:10.1016/j.jamda.2010. 08.002. 36. Onder, G., Carpenter, I., Finne-Soveri, H., Gindin, J., Frijters, D., Henrard, J. C., et al. (2012). Assessment of nursing home residents in Europe: The services and health for elderly in long term care (SHELTER) study. BMC Health Services Research, 12(5), 1–10. doi:10.1186/1472-6963-12-5. 37. Iris, M., DeBacker, N. A., Benner, R., Hammerman, J., & Ridings, J. (2012). Creating a quality of life assessment measure for residents in long term care. Journal of the American Medical Directors Association, 13(5), 438–447.
Nursing home resident quality of life: testing for measurement equivalence across resident, family, and staff perspectives Judith Godin1,2 • Janice Keefe3 • E. Kevin Kelloway4 • John P. Hirdes5
Accepted: 7 April 2015 Ó Springer International Publishing Switzerland 2015
Abstract Purpose This study explores the factor structure of the interRAI self-report nursing home quality of life survey and develops a measure that will allow researchers to compare predictors of quality of life (QOL) across resident, family, and staff perspectives. Methods Nursing home residents (N = 319), family members (N = 397), and staff (N = 862) were surveyed about their perceptions of resident QOL. Exploratory factor analyses were conducted on a random half of the staff data. Subsequently, confirmatory factor analysis was used to test for measurement equivalence across the three perspectives. Results The final model had a four-factor structure (i.e., care and support, food, autonomy, and activities) across all three perspectives. Each factor had at least two items that
were equivalent across all three perspectives, which suggests at least partial measurement equivalence. Conclusion The finding of partial measurement equivalence acknowledges there are important differences between perspectives and provides a tool that researchers can use to compare predictors of QOL, but not levels of agreement across perspectives. Targeting these four aspects is likely to have the additional benefit of improving family and staff perceptions of resident QOL in addition to the resident’s own QOL. Keywords Quality of life Measurement equivalence Long-term care Nursing home residents Family perceptions Staff perceptions InterRAI
Introduction
& Judith Godin [email protected] 1
Nova Scotia Centre on Aging, Mount Saint Vincent University, 166 Bedford Highway, Halifax, NS B3M 2J6, Canada
2
Geriatric Medicine Research, Nova Scotia Health Authority, 5955 Veteran’s Memorial Lane, Halifax, NS B3H 2E1, Canada
3
Department of Family Studies and Gerontology, Nova Scotia Centre on Aging, Mount Saint Vincent University, 166 Bedford Highway, Halifax, NS B3M 2J6, Canada
4
Department of Psychology, Saint Mary’s University, 923 Robie Street, Halifax, NS B3H 3C3, Canada
5
School of Public Health and Health Systems, University of Waterloo, 200 University Avenue West, Waterloo, ON N2L 3G1, Canada
Quality of life (QOL) is a comprehensive construct that has been defined differently across contexts [1]. For example, some QOL assessments focus on specific diseases (e.g., quality of life—Alzheimer’s disease), while others focus on health and functional status (e.g., 36-item short-form survey) [2, 3]. Others employ a multidimensional assessment, covering a broader range of QOL domains [4]. QOL is a subjective concept; it encompasses much more than health and functional status [5]. This subjectiveness makes developing a QOL assessment tool that can be used to examine different perspectives (e.g., nursing home resident, family, and staff) very challenging. Staff and family members’ perceptions may differ from the residents’ own perceptions, but may still be of interest to researchers, policy makers, and administrators. In an ideal nursing home environment, residents, family, and staff all perceive resident QOL to be good.
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Different perspectives of resident quality of life
Purpose
Some researchers have argued that the resident perspective is the only appropriate assessment of QOL [6, 7]. Researchers have reported that residents are capable of answering QOL questionnaires even in cases where MiniMental State Exam scores are as low as three [3, 8]. An ongoing debate exists as to whether other people’s perceptions should be taken into consideration when assessing resident QOL [9]. Although the residents’ own perspective may be the most salient and important assessment of their QOL, understanding the perceptions of family members and staff is also important. Caregiver burden may increase if family members believe their relatives are experiencing poor QOL, regardless of the perceptions of the resident [13, 15]. Family members have the potential to advocate for residents. They may be able to influence policy change and draw attention to quality issues, such as housekeeping [16]. Staff perceptions of residents’ QOL may influence how they deliver care and allocate resources [17, 18]. Both family and staff make decisions for residents regarding care and daily activities. These decisions are likely based on a desire to increase or maintain the resident’s QOL. Although another person’s perceptions should never be used as a substitute or proxy for a resident’s own report of QOL, information from family and staff could add context and paint a more complete picture of QOL within an organization. In response to the argument that residents report good QOL because they have acclimated to their environment, Kane and colleagues argue that this acclimation can be seen as successful coping—a key component of QOL [9]. Still, others’ perceptions could be used to expand the evidence regarding resident QOL. For example, a resident may report satisfaction with food because he or she has learned to cope with the new environment. Introducing a favorite meal into the menu based on a family member’s suggestion may improve resident satisfaction and mood. Most of the research examining different perspectives of QOL has focused on proxy reports. However, such reports can differ substantially from family and staff perceptions about another individual’s QOL. Specifically, when using proxies, family and staff are asked to respond to the questions as they feel the resident would respond [9]. While these perspectives of QOL are valid and can provide additional information [10], they should not be equated with the resident’s views by default. As such, in the current research, we asked nursing home staff and family members about their perceptions of resident QOL because we viewed each perspective as distinct and important.
Our research develops a measure of nursing home resident QOL that can be used to assess staff and family perceptions in addition to the residents’ own perceptions of QOL. Although the actual distributions of ratings on summary scales may be different across the three perspectives, this new measure needs to tap similar domains of QOL in order to allow meaningful comparisons across perspectives regarding predictors of QOL. The importance of family and staff perceptions of resident QOL has received only limited attention in the literature. To the best of our knowledge, no studies have reported on a measure of QOL that has been explicitly assessed for measurement equivalence. Our research addresses this gap in the literature by forwarding a new tool that can be used to assess resident, family, and staff perceptions of resident QOL.
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Methods We recruited resident, family, and staff participants from 23 publicly funded nursing homes across Nova Scotia, Canada. Subsequently, the invitation to participate was extended to staff from other nursing homes in Nova Scotia. Each of these participant groups completed a survey that assessed perceptions of resident QOL. Ethics review boards of all participating universities and, where appropriate, participating nursing homes and regional health authorities approved the research. All responses were kept confidential and were identified only at the facility level, with the exception that respondents who indicated they would be interested in participating in follow-up research provided their names and contact information. After data collection, this identifying information was separated from the survey data. Procedure We sent recruitment letters to potential resident, family member, and staff participants. The letters contained background information on the project, identified when onsite information sessions would take place, and outlined how to take part in the survey or find out more information about the project. The introduction to the survey informed family and staff participants that we were interested in their perspective as a family member or staff (e.g., this survey asks you to share your perspective as a staff member of a nursing home in Nova Scotia). This statement was included to ensure that responses
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to the survey reflected their own perceptions. The residents, family members, and staff were not linked as triads. Residents of any age who lived in the nursing home for at least 1 month could participate in the study. Recruitment letters were hand delivered to all residents regardless of cognitive ability. If staff felt the letter might cause distress, they could choose not to give the letter to the resident. Residents who participated in the survey completed an informed consent procedure with a research assistant. The research assistant asked the resident questions about the consent form to ascertain that the resident understood the consent information. If the questions were answered appropriately, the research assistant would proceed with the informed consent and survey. If the resident could not answer any of the questions, the research assistant thanked the resident for his or her interest and closed the survey. Consent was audio recorded for situations in which written consent was not possible. In cases where neither written nor audio consent was possible, the resident indicated consent in their usual manner of communicating (e.g., head nod, pointing to a ‘yes’ icon on a board or typing). The first page of the family and staff surveys provided the information necessary for informed consent. Survey completion and return implied consent. Family and staff were offered either paper or online versions of the survey. Because we do not have accurate numbers regarding how many people were invited to respond, we do not have exact response rates. Instead, participation rates were approximated by comparing the number of responses to the total number of beds and the total number of staff at participating facilities. Based on this, 13 % of residents, 16 % of family members, and 11 % of staff participated in the survey. Participants
had lived in their current facility for more than a year (73 %). Three-quarters of these residents were female (77 %) and between the ages of 75 and 94 (79 %). Staff Of the 862 staff who responded to the survey, most were female (91 %). Ages ranged from 16 to 71, with an average age of 43 (SD = 11.7). Thirty-eight percent were continuing care assistants (CCAs), 18 % were registered or licensed practical nurses, 3 % were other care staff, such as occupational therapists, 21 % were support staff (e.g., housekeeping and dietary), 13 % were in administration, and 7 % did not report their job title. Two-thirds of staff respondents were full time (66 %), one-quarter were part time (26 %), and the remainder were casual.
Measures Resident quality of life The interRAI self-report survey on nursing home quality of life was used in the resident, family, and staff surveys [11]. The resident version has been used mainly with residents who scored from 0 (intact) to 3 (moderate impairment) on the Cognitive Performance Scale [12]. The survey was designed to be administered to nursing home residents [11], and we adapted the survey for family and staff perspectives. We altered the items slightly for each perspective (e.g., I feel safe around those who provide me with support and care, My family member is safe around those who provide him/her with support and care, and Residents are safe around those who provide their support and care). Response options were on a scale of 0 ‘never’ to 4 ‘always.’
Nursing home residents Additional variables Of the 319 residents who responded to the survey, most were female (73 %) and between the ages of 75 and 94 (62 %). Seven percent were 95 years of age or older, and 30 % were under the age of 74. A large number of residents were in their current nursing home for longer than 2 years (46 %). Family members Of the 397 family members who responded to the survey, most were female (78 %) and between the ages of 55 and 74 (68 %). Three-quarters were married (79 %). Over half were retired (55 %), and a quarter worked full time (27 %). Over half (58 %) were the primary caregivers of the resident prior to admission. Two-thirds (64 %) of family members were the child of the nursing home resident. Most of the residents that the family members were referencing
Three variables not included in the main analyses were used to impute missing data. Two single-item measures were used: ‘How would you describe your overall quality of life?’ and ‘Given your health status today, how would you describe your overall experience of living in this nursing home?’ The wording of these items was adapted to reflect the appropriate perspective in the family and staff surveys. There were five response options for these questions that ranged from ‘very poor’ to ‘very good,’ with a middle category of ‘neutral.’ Each participating nursing home belonged to one of three models of care based on characteristics of its physical design and staffing. Specifically, participants were from a traditional facility (i.e., traditional physical design, CCAs provide care needs only), a new augmented facility (i.e.,
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neighborhood design, CCAs provide care and do some other tasks, such as housekeeping), or a new full-scope facility (i.e., neighborhood design, CCAs responsible for care, housekeeping, and dietary tasks). Statistical procedures Missing data Missing data on the QOL items ranged from 4.2 to 17.9 % (M = 6.3 %, SD = 2.06 %) in the staff data, 1.5 to 50.1 % (M = 17.3 %, SD = 11.01 %) in the family data, and 1.3 to 21.3 % (M = 6.8 %, SD = 4.91 %) in the resident data. For the item that was missing 50.1 % of the data (i.e., residents/staff asked before using my family member’s things), 142 (35.8 %) indicated that they did not know and 47 (11.8 %) said that the question was not applicable. The remaining 10 family members skipped the question. The most widely used method of handling missing data is listwise deletion; however, listwise deletion is known to produce biased and inaccurate results [13, 14]. Multiple imputation (MI) provides less biased and more accurate estimates [15–18]; thus, with the NORM software [19], we used multiple imputation to address missing data. All variables included in the main analyses must be used in the imputation model, and including additional variables is beneficial [15, 16, 20]. We included in the 49 interRAI QOL items, perspective, the single-item measure of QOL and nursing home experience, and model of care in the imputation model. The exploratory and the initial confirmatory factor analyses were conducted on a single imputed dataset [18]. The final model was tested on five imputed datasets, and the results were pooled according to the guidelines outlined by Enders [15]. Exploratory factor analyses (EFA) We were interested in conducting both exploratory and confirmatory analyses; however, conducting these analyses on the same data is inappropriate. The staff dataset was the only dataset large enough to randomly split into two halves: one for exploratory analyses and one for confirmatory analyses (n = 432 and n = 430, respectively). This large sample size provided the most reliable exploratory analyses. The EFAs were conducted using IBM SPSS Statistics 20. Prior to conducting each EFA, we conducted Velicer’s minimum average partial and parallel analyses to determine the number of factors [21]. Generalized least squares was used to extract the factors with direct oblimin rotation. In order to be retained, items had to have a factor loading of six or higher in either the pattern or structure matrix, had to load onto the same factor in both matrices, and could not crossload onto multiple factors in the pattern matrix.
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Confirmatory factor analysis All confirmatory factor analyses were conducted using EQS version 6.1 for Windows. In order to optimize the model for each group, we first tested the hypothesized model (see Fig. 1) separately with each perspective (i.e., staff, family, and resident) [22, 23]. Due to high normalized Mardia’s coefficients (NMC = 21.70, 49.90, and 40.63 for staff, family, and resident perspectives, respectively) [23], we reported robust fit statistics and used robust standard errors in the calculation of significance tests. A comparative fit index (*CFI) [.90, a root mean squared error of approximation (*RMSEA) \.08, and a standardized root mean square residual (*SRMR) \.10 indicated good fitting models. We adjusted the hypothesized model for each perspective based on the results of Lagrange’s multiplier tests (LMTest), standardized residuals, and theoretical considerations. For example, the item ‘Residents can eat when they want’ was removed from the staff perspective because the LMTests and standardized residuals indicated this item was contributing to poor fit and it made intuitive sense that staff may not view this item as important if they do not receive any complaints from residents. DSantora–Bentler (S–B)v2 was used to assess each model modification [24]. Cheung and Rensvold’s recommendation of a change in CFI of more than .01 was used to assess changes in fit due to adding or removing constraints [25].
Results Exploratory factor analysis on staff data We conducted a series of EFAs on a random half of the staff data. For each EFA, items that did not meet the criteria specified above were removed and a subsequent EFA was conducted. These procedures were repeated until all items met the criteria for retention. The excluded items are listed in Table 1. Minimum average partial and parallel analyses on the final 29 items indicated four components were present. Care and support, satisfaction with food, autonomy, and activities emerged in the final EFA and accounted for 54 % of the variance. Confirmatory factor analysis Staff perspective All estimated parameters were significant at the p \ .05 level; however, fit statistics were less than ideal (see Table 2). We made three sequential modifications to the
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Resident Quality of Life
privacy is respected
privacy of personal information
feeling safe among staff
able to be alone
getting needed help
can visit in private
get needed services
eat when want
staff attention
free to go outside
Care and Support
expressing opinion
Autonomy decision going to bed
decision spending time
treated with dignity by staff
free to go when/where they want
likes are respected
Food
staff responsiveness
Activities
control access to room
can have bath or shower
timely delivery of services
living life the way they want
like the food
enjoy mealtimes
favourite foods
variety in meals
enjoyable weekend activities
keep mentally active
off unit activities
meaningful activities
Fig. 1 Hypothesized model for confirmatory factor analyses on staff, family, and resident data
hypothesized model: ‘When residents have company, they can visit in private,’ ‘Residents’ personal information is kept private,’ and ‘Residents can eat when they want’ were deleted. Family perspective The fit statistics were less than ideal for the hypothesized model (see Table 3), but all estimated parameters were significant at p \ .05. We made two sequential modifications to the hypothesized model: ‘My family member’s personal information is kept private’ and ‘When my family member has company, he/she can visit in private’ were deleted.
Resident perspective All the estimated parameters were significant at p \ .05; however, the fit statistics were less than ideal (see Table 4). We made two sequential modifications: ‘When I have company, I can visit in private’ and ‘I can be alone when I wish’ were deleted from the model. Measurement invariance across the three perspectives When the model was tested simultaneously across the three perspectives (i.e., configural equivalence), the model fit the data reasonable well (see Table 5). The *CFI dropped by
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Qual Life Res Table 1 Items excluded based on the EFA results
Original QOL domain
Item
Safety/security
Possessions are safe Safe when alone Ask to use my things
Comfort
Recommend site to others Feels like home Bothered by noise
Autonomy
Decide how to spend money
Respect
Careful what I say around staff
Responsive staff
Act on suggestions
Staff–resident bonding
Know the story of my life Time to have a conversation with me Know how to meet my needs Staff member a friend Open and honest
Activities Personal relationships
Religious activities that have meaning to me Another resident is my friend People want to do things with me Ask for my help Important role in people’s lives Opportunities for romance
Table 2 Goodness of fit statistics for the staff perspective
v2
dfs
S–Bv2
*AIC
SRMR
*CFI
*RMSEA (90 % CI)
1203.04
457.04
.073
.843
.072 (.067, .076)
.871
.066 (.061, .071)
.066
.886
.064 (.058, .069)
.063
.900
.061(.055, .066)
Hypothesized model 1356.11
373
Removal of ‘When residents have company, they can visit in private’ DS–Bv2 = 210.20, p \ .001 1122.11
346
994.81
302.81
.067
Removal of ‘Residents’ personal information is kept private’ DS–Bv2 = 112.67, p \ .001 971.10 320 874.30 234.30 Removal of ‘Residents can eat when they want’ DS–Bv2 = 116.00, p \ .001 842.81
295
758.52
168.53
2
Values for DS–B v were calculated using the procedures recommended by [26]
Table 3 Goodness of fit statistics for the family perspective
v2
dfs
Hypothesized model 1285.12 373
S–Bv2
*AIC
SRMR
*CFI
*RMSEA (90 % CI)
1049.93
303.93
.069
.862
.068 (.063, .072)
.880
.066 (.061, .071)
Removal of ‘My family member’s information is kept private’ DS–Bv2 = 101.93, p \ .001 1122.16
346
939.21
247.22
.068
Removal of ‘When my family member has company, he/she can visit in private’ DS–Bv2 = 129.59, p \ .001 943.58
320 2
799.73
159.73
.066
.902
Values for DS–Bv were calculated using the procedures recommended by [26]
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.062 (.056, .067)
Qual Life Res Table 4 Goodness of fit statistics for the resident perspective
v2
S–Bv2
dfs
*AIC
SRMR
*CFI
*RMSEA (90 % CI)
.071
.862
.051 (.045, .057)
.066
.893
.045 (.038, .052)
.065
.905
.044(.036, .050)
Hypothesized model 852.54
373
679.56
-66.44
Removal of ‘When I have company, I can visit in private’ DS–Bv2 = 110.11, p \ .001 717.92
346
571.12
-120.88
Removal of ‘I can be alone when I wish’ DS–Bv2 = 58.27, p \ .001 648.65
320
513.75
-126.252
2
Values for DS–Bv were calculated using the procedures recommended by [26]
.009 when we constrained the first-order factor loadings to be equivalent across perspectives. A drop of .009 is close to the suggested cutoff, and a *CFI of .89 indicates a poor fitting model. Further, LMTests for releasing constraints indicated that there were 10 factor loadings that were not equivalent across perspectives. When appropriate constraints of the first-order factor loadings were lifted, model fit was similar to the fit for configural invariance (D*CFI = .002), indicating partial measurement equivalence. Partial measurement equivalence is sufficient to proceed to the test of structural equivalence of the higher-order factor [26]. Care and support, food, and autonomy exceeded the requirements, and activities met the requirements for partial measurement equivalence (i.e., at least two equivalent items per factor [26]). Items that were not equivalent across perspectives were retained as these items likely contain important information regarding the differences across perspectives [27]. Structural invariance across the three perspectives When the second-order factor loadings were constrained to be equal across the three perspectives, there was very little change in fit from either the previous model D*CFI = .003 or the configural equivalence model (D*CFI = .005; see Table 5), indicating that the structure of the second-order
Table 5 Goodness of fit statistics for testing equivalence across the three perspectives
v2
dfs
factor is equivalent across groups. Factor loadings for the final model and internal reliabilities for the factors and resident QOL can be given in Tables 6 and 7, respectively.
Discussion We found partial measurement equivalence across resident, family, and staff perspectives, which enables comparisons of predictors of resident QOL across perspectives. Not surprisingly, this finding also suggests that nursing home residents, family, and staff have somewhat different conceptualizations of resident QOL. Each perspective places varying levels of importance on different aspects of QOL [28]. With the level of measurement equivalence demonstrated in the current research, assessing agreement between groups is not appropriate: The varying levels of importance the perspectives place on individual items is likely to bias results [29]. Further, to assess agreement across perspectives, we would have to test for a stronger level of equivalence. Partial measurement equivalence does allow for the examination of relations of other variables to QOL within each of these perspectives [23, 29]. The higher-order structure was equivalent across all three perspectives, but there were some discrepancies with the individual items. Of the 29 retained items, only one item, If I
S–Bv2
*AIC
SRMR
*CFI
*RMSEA (90 % CI)
2052.31
182.30
.065
.90
.06 (.05, .06)
.89
.06 (.05, .06)
.90
.06 (.05, .06)
Configural equivalence 2435.04
935
Measurement equivalence (first-order factor loadings constrained) 2618.70
979
2199.09
241.087
.077
Measurement equivalence (some first-order constraints lifted) 2507.76
968
2112.45
176.45
.069
Final model—structural equivalence (constraints added to second-order factor loadings) 2558.59 976 2154.02 202.02 .118 Final model—pooled fit statistics from five imputations
.90
.06 (.05, .06)
2568.17
.90
.06(.05, .06)
976
2464.26
212.26
.121
123
Qual Life Res Table 6 Factor loadings for final model Factor or item
Staff
Family
Resident
Care and support
0.42
0.42
0.42
Privacy is respected
0.81
0.81
0.61
Privacy of personal information
Excluded
Excluded
0.46
Feeling safe among staff
0.62
0.62
0.62
Getting needed help
1.04
1.29
0.88
Get needed services
0.93
0.93
0.77
Staff attention
1.05
1.05
1.05
Expressing opinion
1.17
1.17
0.92
Treated with dignity by staff
1.00
1.00
1.00
Likes are respected
1.16
1.16
1.16
Staff responsiveness Timely delivery of services
1.14 1.14
1.14 1.14
1.14 1.14
Living life the way they want
1.21
1.21
1.21
Food
0.37
0.37
0.37
Like the food
1.00
1.00
1.00
Enjoy mealtimes
0.97
0.97
0.97
Favorite foods
1.13
1.13
1.13
Variety in meals
1.34
1.04
1.04
Autonomy
0.63
0.63
0.63
Able to be alone
0.60
0.60
Excluded
Eat when want
Excluded
0.88
0.88
Free to go outside
1.00
1.12
1.00
Decision going to bed
1.00
1.00
1.00
Decision spending time
0.94
0.70
0.70
Free to go when/where they want
1.00
1.00
1.35
Control access to room
1.01
1.01
1.01
Can have bath or shower Activities
1.07 0.65
1.07 0.65
1.07 0.65
Enjoyable weekend activities
0.87
0.87
0.65
Keep mentally active
0.95
0.95
0.52
Off unit activities
0.91
0.91
0.91
Meaningful activities
1.00
1.00
1.00
need help right away, I can get it, had a different factor loading for each perspective. The four-factor structure of care and support, food, autonomy, and activities loading onto resident QOL was equivalent across the three perspectives, and over 50 % of the items were equivalent across all three perspectives. For pairs of perspectives, 55 % were equivalent between staff and residents, 62 % were equivalent Table 7 Cronbach’s alpha for subscales and overall quality of life
123
Factor
between family and residents, and 76 % were equivalent between staff and family perspectives. These differences, however, do not diminish the ability to make comparison of predictors of QOL across perspectives [22, 23]. Noticeably, the family and staff perspectives were more similar to each other than to the resident perspective as has been found in other research [30]. When assessing someone else’s QOL, raters may report on how they would feel if they were in the residents’ situation and place less emphasis on how residents report they feel. This type of perspective taking may account for some of the differences between residents’ perspectives and family and staff perspectives. Past research provides support for the four factors (i.e., care and support, food, autonomy, and activities). Researchers have identified autonomy and meaningful activities as key components of QOL [28, 31, 32]. Security and safety [9], comfort [28, 31], personal attention [4], and care [32] have been identified as aspects of QOL and are reflected in the care and support factor. Further, care and support and autonomy are considered important aspects of person-centered care [33]. Dementia patients, family members, and staff all identified meaningful activities as an important component of person-centered care [34]. Food has been found to be a component of multidimensional assessments of QOL [4], and researchers have found that food enjoyment was associated with overall satisfaction with the nursing home [35]. The interRAI self-report survey of nursing home quality of life [11], which was used in this research, does not include health or functional status because these are addressed by the clinical assessment instruments such as the interRAI long-term care facility assessment [36]. When asked to describe nursing home resident QOL, focus groups of residents, family, and staff all emphasized more subjective components, such as dignity and autonomy, and placed little focus on more ‘objective components,’ such as health and functional status [37]. Residents’ perceptions of their own QOL are important and cannot be replaced by family or staff perceptions when assessing policy and interventions designed to improve resident QOL. Understanding family and staff perceptions, however, still provides a valuable contribution to research into long-term care outcomes. Family members can
Staff (first half/second half)
Family
Resident
Care and support
.91/.90
.93
.87
Food
.85/.84
.87
.83
Autonomy
.86/.85
.88
.75
Activities
.85/.81
.83
.66
Overall quality of life
.78/.75
.82
.69
Qual Life Res
experience increased distress if they believe that their loved one is experiencing poor QOL. Family members advocate for residents through their communication with administration and staff at long-term care facilities. Staff members decide how to interact with and care for residents based on their own perceptions or residents’ QOL. Understanding others’ perspectives of resident QOL is important even if the perspectives cannot be used to directly assess resident QOL. Limitations We found a four-factor structure of QOL using the staff data. Our hypothesized model may have differed had we conducted the EFA on a different perspective. We started from the staff perspective because the staff sample was large enough to randomly split the data into two halves. Splitting the data enabled us to conduct the CFAs on different data from the EFAs. Even though the four-factor structure was developed from the staff data, the structure was equivalent across the three perspectives. Fifteen of the individual items were equivalent across all perspectives. The factors that emerged in the current study may not represent the breadth of residents’ experience of QOL; however, these factors do represent aspects of QOL that can be assessed across the three perspectives. Because residents needed to have sufficient cognitive ability to provide consent, the findings from the resident perspective may not generalize to residents with severe cognitive impairment. Further, the truncated range of cognitive impairment in the resident perspective meant there was variability between perspectives in the ranges of cognitive ability. Although residents with severe cognitive impairment could not participate, family member participants were able to respond even if the resident had severe cognitive impairment and staff members were answering the questions based on ‘residents’ in general. These differences between perspectives may have contributed to measurement inequivalence. A substantial amount of data were missing and handled through multiple imputation. Although a certain amount of uncertainty is inherent when conducting analyses with any amount of missing data, multiple imputation is a state-ofthe-art technique that produces accurate estimates and takes into consideration this uncertainty [15, 16, 18].
Conclusion Nursing home staff, family members, and residents shared some similarities in their conceptualizations of resident QOL. The four factors that emerged (i.e., care and support, autonomy, food, and activities) can be targeted by nursing homes aiming to improve resident QOL. Nursing home
residents often have poor health and some cognitive impairment, and these elements of their lives are extremely difficult to change; thus, focusing on other aspects is important for improving resident QOL. Targeting these four aspects is likely to have the additional benefit of improving family and staff perceptions of resident QOL. This paper presents a conceptualization of QOL that is similar across the perspectives, thus enabling a greater understanding of how predictors of QOL vary across perspectives. The finding of partial measurement equivalence acknowledges differences across perspectives and at the same time provides a tool that researchers can use to compare predictors of QOL. Although future research is needed to further validate the tool for each of the perspectives, having a tool that is at least partially equivalent enables researchers to better understand what predicts different perceptions of QOL. Acknowledgments This work was supported by a Partnerships for Health System Improvement Grant funded by the Canadian Institutes of Health Research (FRN # 114120) and the Nova Scotia Health Research Foundation (Matching-2011-7173) and a Nova Scotia Health Research Foundation 2013 Scotia Support Grant (PSO-Research Programs-2013-9039). John Hirdes was supported through the Ontario Home Care Research and Knowledge Exchange Chair funded by the Ontario Ministry of Health and Long-Term Care. Conflict of interest
None.
Ethical standard Ethics review boards of all participating universities and, where appropriate, participating nursing homes and regional health authorities approved the research; therefore, this research has been performed in accordance with the ethical standards laid down in the 1964 Declaration of Helsinki and its later amendments.
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