who attends senior centers?

This article was downloaded by: [University of Bath]On: 09 October 2014, At: 06:35Publisher: RoutledgeInforma Ltd Registered in England and Wales Registered Number: 1072954Registered office: Mortimer House, 37-41 Mortimer Street, London W1T 3JH,UK

Journal of Social ServiceResearchPublication details, including instructions forauthors and subscription information:http://www.tandfonline.com/loi/wssr20

Who Attends Senior Centers?Robert J. Calsyn a & Joel P. Winter ba University of Missouri , St. Louis, MO, 63121-4499,USAb Industrial/Organizational Psychology at theUniversity of Missouri , St. Louis, MO, 63121-4499,USAPublished online: 17 Oct 2008.

To cite this article: Robert J. Calsyn & Joel P. Winter (2000) Who Attends SeniorCenters?, Journal of Social Service Research, 26:2, 53-69

To link to this article: http://dx.doi.org/10.1300/J079v26n02_03

PLEASE SCROLL DOWN FOR ARTICLE

Taylor & Francis makes every effort to ensure the accuracy of all theinformation (the “Content”) contained in the publications on our platform.However, Taylor & Francis, our agents, and our licensors make norepresentations or warranties whatsoever as to the accuracy, completeness,or suitability for any purpose of the Content. Any opinions and viewsexpressed in this publication are the opinions and views of the authors, andare not the views of or endorsed by Taylor & Francis. The accuracy of theContent should not be relied upon and should be independently verified withprimary sources of information. Taylor and Francis shall not be liable for anylosses, actions, claims, proceedings, demands, costs, expenses, damages,and other liabilities whatsoever or howsoever caused arising directly orindirectly in connection with, in relation to or arising out of the use of theContent.

This article may be used for research, teaching, and private study purposes.Any substantial or systematic reproduction, redistribution, reselling, loan,

http://www.tandfonline.com/loi/wssr20

http://dx.doi.org/10.1300/J079v26n02_03

sub-licensing, systematic supply, or distribution in any form to anyone isexpressly forbidden. Terms & Conditions of access and use can be found athttp://www.tandfonline.com/page/terms-and-conditions

Dow

nloa

ded

by [

Uni

vers

ity o

f B

ath]

at 0

6:35

09

Oct

ober

201

4

http://www.tandfonline.com/page/terms-and-conditions

Who Attends Senior Centers?Robert J. CalsynJoel P. Winter

ABSTRACT. Logistic regression analysis was used to compare users andnon-users of senior centers. Variables assessing linkage to the servicesystem were more significant predictors of senior center utilization thanpredisposing, enabling, or need variables. More specifically, users of se-nior centers were older and more likely to live in rural areas. They alsohad more social contacts, better mental health, and fewer ADL problems.Senior center users were also more aware of specific service agencies,more likely to consult formal resources in making service decisions, andmore likely to have used other services. [Article copies available for a feefromTheHaworth Document Delivery Service: 1-800-342-9678. E-mail address:[email protected] <Website: http://www.haworthpressinc.com>]

KEYWORDS. Senior centers, behavioral model, service linkage

Senior centers are often considered the preferred access point forthe delivery of older adult services, particularly those funded through

Robert J. Calsyn is Professor of Psychology and Director of Gerontology at theUniversity of Missouri-St. Louis and Joel P. Winter is a doctoral candidate inIndustrial/Organizational Psychology at the University of Missouri-St. Louis.

This study would not have been possible without the cooperation of those olderadults who consented to be interviewed; the authors appreciate their time andthoughtfulness. The authors also wish to thank the staffs of the Missouri Divisionof Aging and the Missouri Department of Social Services, particularly Dr. AnnDeaton and Ms. Becky Viet, who made the data available to the authors. However,the analyses and conclusion presented in this paper are the sole responsibility of theauthors and do not necessarily reflect the views of either agency. The authors alsoappreciate the editorial and word processing assistance of Ms. Dorothy Gano.

Address correspondence to: Dr. Robert J. Calsyn, 406 Tower, University ofMissouri- St. Louis, 8001 Natural Bridge Road, St. Louis, MO 63121-4499.

Submitted: 5/98; Revision Received: 9/98; Accepted: 10/98.

Journal of Social Service Research, Vol. 26(2) 1999E 1999 by The Haworth Press, Inc. All rights reserved. 53

Dow

nloa

ded

by [

Uni

vers

ity o

f B

ath]

at 0

6:35

09

Oct

ober

201

4

JOURNAL OF SOCIAL SERVICE RESEARCH54

the Older Americans Act (Krout, Cutler, & Coward, 1990). However,less than 20% of the older adult population participate in senior centeractivities, even though there are over 10,000 senior centers in theUnited States (Krout, 1989). If the administrators of senior centersknew how non-users of senior centers differed from users of seniorcenters, it might be possible for them to modify their programs andmarketing strategies to better meet the needs of non-users. This studycompared current users and non-users of senior centers using logisticregression analyses.An expanded version of the behavioral model of service utilization

(Andersen, Kravitz, & Anderson, 1975) provided the theoreticalframework for this study. The behavioral model has been the dominantmodel used to predict health and social service utilization, includinguse of senior centers (Krout, Cutler, & Coward, 1990). The modelclassifies predictor variables into three categories: predisposing, enab-ling, and need. Predisposing variables include demographic character-istics as well as attitudes and beliefs about the causes of problems andefficacy of various treatments. Enabling variables include individualresources such as income, transportation, and insurance coverage aswell as family and community resources including social support.Need variables include health, functioning level, morale, and unmetservice needs. Based on the research of Calsyn, Burger, and Roades(1996) this study added another category of predictor variables to thebehavioral model, linkage to the service system. Linkage to the servicesystem includes knowledge of the service system, use of service agen-cies in making service decisions, and other service utilization. Al-though both enabling and linkage to the service system variables areconcerned with issues of resources and access to services, enablingvariables refer to more general resources whereas service linkagevariables focus specifically on the older adult service system.Comprehensive reviews of previous research predicting senior cen-

ter utilization can be found in Krout (1989) and Ralston (1987). Al-though much of the earlier research used small non-representativesamples and failed to use multivariate statistics (Ralston, 1987), sever-al excellent multivariate studies with large representative sampleshave been conducted more recently (e.g., Calsyn, Burger, & Roades,1996; Krout, Cutler, & Coward, 1990). Provided as follows is a briefsummary of this research organized according to the variable catego-ries in the expanded behavioral model.

Dow

nloa

ded

by [

Uni

vers

ity o

f B

ath]

at 0

6:35

09

Oct

ober

201

4

Robert J. Calsyn and Joel P. Winter 55

Predisposing. Age is positively related to senior center utilizationup until about age 85 (Krout, 1989; Ozawa & Morrow-Howell, 1992;Ralston, 1987). Although women use senior centers more than men,the gender effect may merely reflect the larger number of women thanmen in the older adult population (Krout, 1989; Ozawa & Morrow-Howell, 1992; Ralston, 1987). Race has not consistently predictedsenior center utilization (Krout, 1989; Ozawa & Morrow-Howell,1992; Ralston, 1987). Some studies report no race effects; other studiesreport higher utilization among Caucasians, while other studies findgreater use among African-Americans. Although some studies reportno educational differences between users and non-users of senior cen-ters, other studies have found that senior center users have less educa-tion than non-users (Ralston, 1987).Enabling. Income has not consistently predicted senior center uti-

lization in previous research (Ralston, 1987), but the one study withthe most representative sample of U.S. older adults found that usershad less income than non-users (Krout, Cutler, & Coward, 1990).Given the diversity of measures used to measure social contact, thelack of consistent findings in previous research is not surprising. Nev-ertheless, there does appear to be a positive relationship between gen-eral social activity level and senior center utilization (Ralston, 1987).However, prior research has not found a consistent relationship be-tween the level of support from family and friends and senior centerutilization (Ralston, 1987). Most, but not all, studies have found that ahigher percentage of older adults living in urban areas have utilized asenior center than residents living in more rural areas (Krout, 1989).Need. Contrary to the prediction of the behavioral model and re-

search on health services utilization (Wolinsky, 1990), previous stud-ies have consistently found that users of senior centers have fewer, notmore, health needs than non-users (Krout, 1989; Ralston, 1987). Infact, health is one of the most frequent reasons for cessation of seniorcenter involvement (Krout, 1989). Conflicting data exists on whethersenior center users have more mental health needs than non-users(Krout, 1989; Ozawa & Morrow-Howell, 1992). Finally, one studyfound that senior center users reported slightly more unmet serviceneeds than non-users (Calsyn, Burger, & Roades, 1996), similar toother research predicting the use of other social services by the elderly(Starrett & Decker, 1987; Starrett, Dicker, Araujo, & Walters, 1989;Starrett, Todd, & DeLeon, 1989).

Dow

nloa

ded

by [

Uni

vers

ity o

f B

ath]

at 0

6:35

09

Oct

ober

201

4


Linkage to the service system.McCaslin (1989) reported that use ofmost older adult services, including senior centers, was positivelycorrelated with knowledge of the service system and prior serviceutilization. Similarly, Calsyn, Burger, and Roades (1996) found thatsenior center users scored higher than non-users on all four indices ofservice linkage (agency awareness, agency use, prior service utiliza-tion, and use of older adult transportation) in two samples of olderadults.Predictive power of behavioral model. This study also investigated

the aggregate ability of each of the four categories of the expandedbehavioral model (predisposing, enabling, need, and linkage to theservice system) to predict senior center utilization. Andersen, Kravitz,and Anderson (1975) had predicted that need variables would explainmost of the variance of non-discretionary service use such as hospital-ization, whereas predisposing and enabling variables would explainmore of the variance of discretionary service use (e.g., telephonereassurance programs or the use of a senior center). Need variableshave indeed explained most of the variance of health service utiliza-tion by the elderly in previous research (Wolinsky, 1990). Althoughless research has been done predicting use of more discretionary ser-vices by the elderly, several studies do suggest that predisposing andenabling variables explain as much (and sometimes more) variance asneed variables (Calsyn & Roades, 1993; Calsyn, Roades, & Klinken-berg, in press; Mitchell & Krout, 1998; Starrett, Decker, Araujo, &Walters, 1989; Starrett, Todd, & DeLeon, 1989). In addition, servicelinkage variables have typically explained more variance of discre-tionary service use by the elderly than predisposing, enabling, andneed variables (Calsyn & Roades, 1993; Calsyn, Roades, & Klinken-berg, in press; Starrett & Decker, 1987; Starrett, Decker, Araujo, &Walters, 1989; Starrett, Todd, & DeLeon, 1989). To date only onestudy has compared the ability of the four categories in the expandedbehavioral model to distinguish users and non-users of senior centers.In that study the service linkage variables explained the greatest per-centage of the variance of senior center utilization in two representa-tive samples of older adults (Calsyn, Burger, & Roades, 1996). Predis-posing and enabling variables also explained a significant percentageof the variance in one of the samples, but explained minimal variancein the other sample. Need variables were not significant predictors in

Dow

nloa

ded

by [

Uni

vers

ity o

f B

ath]

at 0

6:35

09

Oct

ober

201

4


either sample. The total variance explained by the behavioral modelwas 11% in one sample and 31% in the other sample.Goals of the present study. The present study is an expanded repli-

cation of Calsyn, Burger, and Roades (1996). This study improved onthe previous study in several ways: (1) more social contact variableswere assessed; (2) the sample was larger and representative of anentire state rather a single metropolitan area; (3) the definition ofsenior center user was more restricted. In the prior study anyone whohad ever used a senior center was considered a user even if the respon-dent had not attended a senior center for many years. In hindsight thisdefinition was probably too inclusive, making it more difficult to drawinferences from the data. In the present study only those respondentswho are currently attending a senior center are defined as users. Thismore restricted definition made it easier to interpret the meaning ofsignificant relationships between the predictor variables and seniorcenter utilization.

METHOD

Sample

Sample data were collected for the 1994 needs assessment study ofthe Missouri Department of Social Service’s Division of Aging andthe ten Missouri Area Agencies on Aging (Drainer, 1994). The samplewas stratified by the ten AAA regions in Missouri. Telephone inter-views were conducted by trained interviewers at the Center for Ad-vanced Social Research at the University of Missouri-Columbia.Questions were asked of the person in the household age 60 or overwho had the most recent birthday. A total of 102,500 phone numberswere dialed by computer using random digit dialing. There was noanswer for 24,863 calls; the phone had been disconnected for 21,762calls; a business number was reached for 10,009 calls. There was atotal of 28,456 non-eligible contacts, 6,842 respondents refused theinterview before eligibility could be determined, 1,488 refused theinterview after the eligibility was determined, and 4,095 respondentswho began the interview failed to complete the interview. Thus, thefinal needs assessment sample was 4,985; the 82 cases for which afamily member or someone else had served as a proxy intervieweewere eliminated from this study, leaving a sample of 4,903.

Dow

nloa

ded

by [

Uni

vers

ity o

f B

ath]

at 0

6:35

09

Oct

ober

201

4


Predictor Variables

Refer to Table 1 for the coding and relevant descriptive statistics ofall the variables in the study.Predisposing variables. Predisposing variables included: age, gen-

der, education, and race. Because of small numbers of respondentsfrom other ethnic minority groups, race was coded as a dichotomousvariable, Caucasian and African-American. The 116 other cases (2.4%of the total sample) were not used in the analysis. Marital status wasnot included in the analyses because it was highly correlated (r = .78)with living situation which was retained in the analysis as an enablingvariable.Enabling variables. Enabling variables consisted of income, time

lived in community, urban percentage, and several social contact vari-ables. Because of the large amount of missing data on income (1,275cases), missing data was replaced by the median value. This procedureassumes that missing data is not related to income, an assumption theauthors felt would not be severely violated. Results from an analysiswithout missing data substitution were substantively similiar. Urbanpercentage was derived from county of residence. The percentage ofurban blocks within each county were available from census data(Blodgett, 1996). Thus, urbanization was coded as a continuous vari-able ranging from 0 to 1 indicating the extent of urbanization withinthe respondent’s county of residence. Social contact variables were:amount of social contact, whether respondents had someone whocould care for them as long as needed if they became ill, living situa-tion, number of informal sources of service information, and numberof informal sources for deciding where to get services. Amount ofsocial contact was derived by combining and transforming responsesto two survey items: assessing time spent talking on the phone withfriends or relatives, and time spent visiting with friends or relatives.Both items were measured on scales from 0 (not at all) to 3 (once a dayor more). These items were combined after weighting the visiting itemby a factor of two (due to the relatively greater richness of face-to-faceinteraction) by averaging. Living situation was a dichotomous variableassessing whether respondents lived with others or alone. Respondentswere also asked two open-ended questions concerning who they con-sulted in seeking both service information and making service deci-sions. From responses to these two questions it was possible to count

Dow

nloa

ded

by [

Uni

vers

ity o

f B

ath]

at 0

6:35

09

Oct

ober

201

4


TABLE 1. Descriptive Statistics for Variables in the Study

Variable Coding Mean (Standard Deviation)

Predisposing

Age Age in years 70.83 (7.37)

Gender 0 = Male1 = Female .69 (.46)

Education 1-7, where:1 = Never attended schoolAnd7 = College 4 yearsor more 4.20 (1.53)

Race 0 = White1 = Black .06 ( .24)

Enabling

Income 0 to 7, with0 = < $5,000 and7 = $50,000+ 3.14 (1.70)

Time in Community Years 34.37 (22.56)

Urban Percentage of urban blocksPercentage within each county .62 ( .35)

Emergency Support 0 = No1 = Yes .52 ( .50)

Social Contact Time spent on phoneand visiting awayfrom residence 2.19 ( .67)

Informal Number of informalInformation sources of information .50 ( .50)

Living Situation 0 = Alone1 = With others .60 ( .49)

Need

Perceived Health 1-5 with:1 = Excellent5 = Poor 2.87 (1.18)

Poor mental health Number of days in lastdays 30 that mental health

was not good 1.60 ( 5.37)

Dow

nloa

ded

by [

Uni

vers

ity o

f B

ath]

at 0

6:35

09

Oct

ober

201

4


TABLE 1 (continued)

Variable Coding Mean (Standard Deviationn)

ADL Assistance 0 = No assistance needed1 = Receives some assistance .08 (.27)

lADL Assistance 0 = No assistance needed1 = Receives some assistance .37 (.48)

Perceived 1 = Quite oftenloneliness 2 = Sometimes

3 = Almost never 2.69 (.57)

Unmet service Number of servicesneed reported needed .49 (1.24)

Linkage to the Service System

Formal Number of formalInformation sources of information .62 (.49)

Agency Number of state agenciesawareness respondent is aware of 2.87 (1.35)

Perceived Number of professionalservice services respondentavailability believes available 10.92 (4.91)

Service Number of professionalutilization services respondent is

receiving .80 (1.38)

the number of informal sources consulted in seeking service informa-tion and making service decisions. Informal sources included friends,relatives, neighbors, etc.Need variables. Physical health need variables consisted of per-

ceived overall health, whether respondents needed help with ADLs(activities of daily living), and whether respondents needed help withIADLs (instrumental activities of daily living). Perceived overall

Dow

nloa

ded

by [

Uni

vers

ity o

f B

ath]

at 0

6:35

09

Oct

ober

201

4


health was measured on a 5-point scale with higher scores indicatingpoorer health. Both ADL and IADL were dichotomous indicators; ifrespondents needed help on any activity, they were coded as needinghelp. Possible ADLs were eating, dressing, toileting, bathing, gettingin/out of bed, getting around, and getting outside. Possible IADLsincluded using the telephone, normal housework, heavy cleaning,shopping, managing money, preparing meals, and taking medication.Two variables measured mental health needs: number of days withinthe past month that mental health was not good and perceived loneli-ness measured on a three-point scale. Unmet service need was mea-sured by asking respondents the number of services that they neededbut were not currently receiving. Services listed included: assistancewith personal care, respite care, housekeeping assistance, home nurs-ing care, adult day-time care centers, in-home therapies (respiratory,speech, physical), Meals on Wheels, transportation assistance, reas-surance services, home repair, governmental income supplement, fi-nancial help (paying utilities), assistance filling out insurance andother forms, job-finding, information services, information on caringfor another elderly person, counseling or support groups, health screen-ing, and information on staying healthy (diet, illness prevention).Linkage to the service system. Linkage variables consisted of

awareness of state service agencies, perceived service availability, useof formal sources in seeking information about services, in makingservice decisions, and other service utilization. Agency awareness wasa count of the number of agencies the respondent reported familiaritywith. The possible agencies consisted of: Missouri Division of Aging,Missouri Division of Family Services, Area Agencies on Aging,awareness that each county has a local office for the Division ofAging, and awareness of the hotline number for reporting abuse, ne-glect, or exploitation of the elderly. Perceived service availability con-sisted of a count of the number of professional services with whichrespondents reported familiarity. Other service utilization was simplya count of the number of services which the respondent reportedreceiving. The same list used in measuring unmet service need wasused to measure perceived service availability and service utilization.Use of formal sources in seeking information and making servicedecisions was a simple count of the number of formal sources (e.g.,physicians, social workers, agencies) that the respondent listed in

Dow

nloa

ded

by [

Uni

vers

ity o

f B

ath]

at 0

6:35

09

Oct

ober

201

4


response to two open-ended questions regarding who respondentswould contact for information about services.Dependent variable. Respondents were asked a simple question,

‘‘Do you go to a senior center?’’ which was coded 0 = no, 1 = yes.

Analysis Strategy

A hierarchical logistic regression approach was used. (See Table 2.)This approach allows for an assessment of the relative contribution ofeach set of variables. The sets were entered in the following order:predisposing, enabling, need, and linkage to the service system. Thisordering was determined in part by assumptions of time precedence inthe model. The predisposing variables such as race and age certainlypre-existed before any of the other variable categories. While timeprecedence between other variables is less certain, this framework isconsistent with the theoretical assumptions underlying the expandedbehavioral model. At each step, fit was assessed with the chi-squareimprovement and pseudo-R2 statistics (Hosmer & Lemeshow, 1989).Because of listwise deletion of missing data (other than income, asdiscussed above), the sample size for the analysis was 3,930.

RESULTS

Only 8.3% of the sample were currently attending a senior center.The hierarchial logistic regression analyses indicated a significantimprovement in model fit at each step. Predisposing variables reducedthe null-model chi-square (the 2LL statistic, which equaled2,252.13 in this analyses) by 52.21 (p < .01), pseudo-R2 = .02. At thisstep age and race were significantly related to senior center utilization.Enabling variables improved fit by �2 = 40.07 (p < .01), incrementalpseudo-R2 = .02. At this step, age, social contact, and urbanness weresignificant variables. Need variables contributed least to model fit: �2 =19.70 (p < .05), incremental pseudo-R2 = .01. At this step, ADL andIADL were added as significant variables. The final step, linkage vari-ables, contributed most to model fit: �2 = 101.70 (p < .01), incrementalpseudo-R2 = .05. The total improvement in model fit was: �2 =213.678 (p < .01), incremental pseudo-R2 = .09. Significant variablesin the final step are discussed as follows.

Dow

nloa

ded

by [

Uni

vers

ity o

f B

ath]

at 0

6:35

09

Oct

ober

201

4


TABLE 2. Logistic Regression Coefficients Predicting Senior Center Usage atFinal Step Variables

Predisposing b SE p Odds

Age .041 .009 .001* 1.04

Education .033 .044 .454 .97

Gender .050 .148 .736 1.05

Race .199 .269 .460 1.22

Enabling

Income .005 .044 .915 1.00

% Urban .642 .184 .001* .53

Time in Community .003 .003 .324 1.00

Social Contact .207 .097 .031* 1.23

Emergency .117 .126 .353 .89

Living Situation .113 .141 .424 .89

Informal Information .114 .123 .352 .89

Need

Health .046 .060 .439 1.05

MH Days .027 .013 .046* .97

Loneliness .151 .112 .176 .86

IADL .252 .144 .080 1.29

ADL .918 .245 .001* .40

Service Need .051 .049 .302 1.05

Linkage

Agency Awareness .238 .050 .001* 1.27

Perceived Availability .025 .015 .095 1.03

Service Utilization .259 .039 .001* 1.30

Formal Information .378 .140 .007* 1.46

* p < .05

Dow

nloa

ded

by [

Uni

vers

ity o

f B

ath]

at 0

6:35

09

Oct

ober

201

4


Only one of the predisposing variables was a significant (p < .05)predictor of senior center utilization. Older respondents were morelikely to attend a senior center. Two of the enabling variables weresignificant predictors of senior center utilization. Respondents whowere more active socially and/or living in a less urban area were morelikely to attend a senior center. Two need variables were significantpredictors of senior center utilization. Those respondents who reportedfewer days when their mental health was not good and those respond-ents who had fewer ADL problems were more likely to attend a seniorcenter. Three of the service linkage variables were significant predic-tors of senior center utilization. Respondents were more likely toattend a senior center if they: (1) were aware of more agencies; (2) hadused formal agencies in seeking service information, and (3) had usedmore older adult services.

DISCUSSION

Integration of Findings with Past Research

Past research had suggested that predisposing and enabling vari-ables often explain as much variance as need variables in the use ofdiscretionary service, including senior centers. This study did notsupport that research. In this study the service linkage variables werebetter predictors of senior center utilization than all of the variables ofthe original behavioral model combined, replicating the previousstudy by Calsyn, Burger, and Roades (1996). However, in this studythe full model explained only 10% of the variance of senior centerutilization, whereas the full model had explained between 11-31% ofthe variance of senior center utilization in the samples used in the priorstudy (Calsyn, Burger, & Roades, 1996).This study replicated most past research with regard to the predic-

tive ability of specific predisposing variables. Race and gender had notbeen consistent predictors of senior center use in previous research;there was no effect of either variable in this study. Consistent with pastresearch, age was positively correlated with senior center utilization.Contrary to past research, respondents who lived in more rural areaswere more likely to report that they attended a senior center than theirurban counterparts. This finding must be regarded with some skepti-

Dow

nloa

ded

by [

Uni

vers

ity o

f B

ath]

at 0

6:35

09

Oct

ober

201

4


cism, however, because of the rather imprecise way we measuredurbanization in this sample (a county-wide estimate rather than acensus tract estimate).Enabling variables, including income and most indices of social

support, were not significant predictors of senior center utilization inprevious studies, nor in this study. Greater social activity had been theone enabling variable which had usually predicted senior center uti-lization in previous studies; this study replicated that finding. Thisstudy supported past research which had found that users of seniorcenters typically have fewer physical health needs than non-users. Pastresearch reported conflicting data regarding the impact of mentalhealth needs on senior center usage. This study also provided conflict-ing data. Those respondents who reported having fewer days whentheir mental health was not good were more likely to attend a seniorcenter. However, the other indicator of mental health need, feelings ofloneliness, was unrelated to senior center utilization. One previousstudy had found that senior center users reported slightly more unmetservice needs (Calsyn, Burger, & Roades, 1996); this study failed tosupport that finding.Consistent with Calsyn, Burger, and Roades (1996), the linkage

variables explained more of the variance of senior center use than theother categories in the expanded behavioral model. Agency awarenessand other service utilization were positively related to senior centerutilization, replicating past research. In addition, the use of formalagencies in getting service information was correlated with seniorcenter usage. However, it is important to remind the reader that thisstudy, and nearly all of the past research on senior center utilization,has been based on cross-sectional data, so one cannot draw firm con-clusions about the causal relationship between the linkage variablesand senior center attendance. In all likelihood the causal relationship isprobably reciprocal.The above summary indicates that our results replicated much of

the previous research on senior center utilization, but, as noted in theintroduction, many inconsistent findings can be found in previousstudies. Sampling differences are responsible for some of the studydiscrepancies. Also, the composition of predictor variables variesfrom study to study. No one study has a comprehensive set of predic-tor variables, assessing all of the possible predisposing, enabling,need, and service linkage variables. Therefore, given the shared vari-

Dow

nloa

ded

by [

Uni

vers

ity o

f B

ath]

at 0

6:35

09

Oct

ober

201

4


ance between many predictor variables, it is not surprising that avariable identified as a significant predictor in one study may not be asignificant predictor in another study. A related fact is that the opera-tional definitions of many of the predictor variables, particularly thosemeasuring social contact and social support, vary dramatically fromstudy to study. Finally, the operational definition of the dependentvariable, senior center use, has varied across studies. The most inclu-sive definition is exemplified by Calsyn, Burger, and Roades (1996)who defined use as attendance at any time during one’s life. Thepresent study employed the most restrictive definition; users had to becurrently attending a senior center. Krout, Cutler, and Coward (1990)took an intermediate position; users were defined as anyone attendinga senior center within the past 12 months. Each of these definitions hasits strengths and weaknesses; the choice of definition depends in parton the question being addressed. Future researchers, however, areprobably well advised to assess at least the following three indicatorsof use in order to better understand the dynamics of senior centerutilization: (1) lifetime usage (yes/no), (2) current usage, includingfrequency of attendance, and (3) length of time since last attended asenior center.Finally, given that the behavioral model has not explained much of

the variance of senior center utilization, future researchers shouldconsider using other theoretical models in predicting attendance at asenior center. It may be that attending a senior center is more likeattending a social club than using a service. If so, theoretical modelsdeveloped to predict membership in voluntary associations may bemore successful in predicting attendance at a senior center than thebehavioral model. For example, the model developed by McPherson,Pobielasz, and Drobnic (1992) applies social network concepts topredict participation in voluntary groups. This theoretical and statisti-cal approach may offer a better way to understand who decides toattend a senior center and for what duration. In addition, research onparticipation in leisure activities may also have applicability to theprediction of senior center attendance (Kelly, 1993). Early research onparticipation in leisure and voluntary activities by older adults wasoften guided by activity theory and focused on life satisfaction (Bull &Aucoin, 1975; N. Cutler, 1981-82; S. Cutler, 1976). However, at leastone prior study examined how participants in different leisure groups(including senior centers) differed from each other (Mellinger & Holt,

Dow

nloa

ded

by [

Uni

vers

ity o

f B

ath]

at 0

6:35

09

Oct

ober

201

4


1982). Recently, some researchers have tried to use motivationaltheory to predict older adults’ participation in various leisure activities(Losier, Bourque, & Vallerand, 1993). In the final analysis a hybridmodel which integrates some concepts from the behavioral modelwith social network theory and models of leisure may be needed toadequately explain senior center attendance.

Implications for Service Providers

The study has only modest implications for service delivery, giventhat the model only explained a small percentage of the variance ofsenior center attendance. Because administrators are always con-cerned with service barriers and issues of discrimination, the fact thatrace and gender were not significant predictors of senior center utiliza-tion should offer some comfort to the administrators of local seniorcenters and State Units on Aging. The positive correlation betweenage and senior center utilization has both positive and negative im-plications. Older senior citizens frequently do need more services thantheir younger counterparts, so their greater usage of senior centersseems appropriate. However, failure to attract younger senior citizensto the centers may have some negative consequences. First, youngerseniors can be an important source of volunteers for delivering pro-grams to more needy older adults. Second, unless senior citizens havesome contact with the senior center when they are younger, they mightnot identify the senior center as a resource when their service needsincrease. Administrators may want to investigate why urban olderadults appear less likely to attend senior centers than rural older adults,even though this finding may be partially the result of a measurementartifact. Our data indicate that urban senior citizens were no less awareof older adult services than rural seniors. Similarly, urban seniors wereno more likely to use other services than rural seniors.The fact that income did not predict senior center usage can be

viewed as a positive outcome from a service access point of view. Thefinding that the seniors who were the least active socially were alsoless likely to attend senior centers indicates that senior centers need todevelop better outreach techniques to identify isolated older adults.The fact that most of the need variables were either negatively relatedor unrelated to senior center utilization is undoubtedly disappointingto those policy makers who believe that the senior center should be theprimary access point for service delivery under the Older Americans

Dow

nloa

ded

by [

Uni

vers

ity o

f B

ath]

at 0

6:35

09

Oct

ober

201

4


Act. However, research has consistently shown that the neediest olderadults (e.g., those in poor physical health) are often not served bysenior centers. Nevertheless, senior centers can be important serviceaccess points, if they provide information and referral to other services(e.g., home delivered meals and homemaker services) which are fre-quently needed by more frail older adults (Krout, 1989).The service linkage variables, particularly agency awareness and

other service utilization, were the best predictors of senior centerutilization. Thus, any intervention which has a positive impact onservice linkage variables will probably also increase senior centerutilization. Thus, increased marketing of all senior services throughnewspapers, brochures, and special information days for both seniorsand human service providers seems warranted. In addition, AreaAgencies on Aging may want to develop several inexpensive, butpopular services, which may entice senior citizens to come in contactwith the older adult service network. A senior citizen discount cardfunded by local merchants and distributed by agencies in the servicenetwork, including senior centers, is one such service. Sponsoringhealth information days at local shopping malls is another idea. Themailing lists developed from these inexpensive services can be used infuture outreach efforts to involve older adults in a variety of services,including senior centers.

REFERENCES

Andersen, R., Kravitz, J., & Anderson, O. (1975). Equity in health services. Cam-bridge, MA: Ballinger Publishing Company.

Blodgett, J. (1996). MABLE/GEOCORR Geographic Correspondence Engine(http://www.oseda.missouri.edu/uic). University of Missouri-St. Louis, St. Louis,MO: Urban Information Center.

Bull, C.N., & Aucoin, J. (1975). Voluntary association participation and life satisfac-tion: A replication note. Journal of Gerontology, 30, 73-76.

Calsyn, R.J., & Roades, L.A. (1993). Predicting perceived service need, service aware-ness, and service utilization. Journal of Gerontological Social Work, 21, 59-76.

Calsyn, R.J., Roades, L.A., & Klinkenberg, W.D. (in press). Using theory to improveneeds assessment studies of the elderly. Evaluation and Program Planning.

Calsyn, R.J., Burger, G.K., & Roades, L.A. (1996). Cross-validation of differencesbetween users and non-users of senior centers. Journal of Social Service Re-search, 21(3), 39-56.

Cutler, N.E. (1982). Voluntary association participation and life satisfaction: Replica-tion, revision and extension. International Journal of Aging and Human Develop-ment, 14, 127-137.

Dow

nloa

ded

by [

Uni

vers

ity o

f B

ath]

at 0

6:35

09

Oct

ober

201

4


Cutler, S.J. (1976). Age differences in voluntary association memberships. SocialForces, 55, 43-58.

Drainer, M.D. (1994). Needs assessment study: 1994 Statewide report. MissouriDepartment of Social Services, 221 West High Street, Jefferson City, MO 65101.

Hosmer, D.W., & Lemeshow, S. (1989) Applied logistic regression. New York: Wiley.Kelly, J.R. (1993). Activity and aging: Staying involved in later life. Newbury Park,

CA: Sage.Krout, J. (1989). Notes on policy and practice: Utilization of services by the elderly.

Social Service Review, 58, 281-290.Krout, J., Cutler, S., & Coward, R. (1990). Correlates of senior center participation:

A national analysis. The Gerontologist, 30, 72-79.Losier, G.F., Bourque, P.E., & Vallerand, R.J. (1993). A motivational model of

leisure participation in the elderly. The Journal of Psychology, 127, 153-170.McCaslin, R. (1989). Service utilization by the elderly: The importance of orienta-

tion to the formal system. Journal of Gerontological Social Work, 14, 153-174.McPherson, J.M., Popielarz, P.A., & Drobnic, S. (1992). Social networks and organi-

zational dynamics. American Sociological Review, 57, 153-170.Mellinger, J., & Holt, R. (1982). Characteristics of elderly participants in three types

of leisure groups. Psychological Reports, 50, 447-458.Mitchell, J., & Krout, J.A. (1998). Discretion and service use among older adults:

The behavioral model revisited. The Gerontologist, 38, 159-168.Ozawa, M.N., & Morrow-Howell, N. (1992). Service utilization by well-organized

frail elderly individuals. International Journal of Aging andHumanDevelopment,35, 179-191.

Ralston, P. (1987). Senior center research: Policy from knowledge? In E. Borgotta &R. Montgomery (Eds.), Critical issues in aging policy: Linking research andvalues. Newbury Park, CA: Sage.

Starrett, R.A., & Decker, J.T. (1987). The utilization of social services by the Mexi-can-American elderly. Journal of Gerontological Social Work, 9, 87-101.

Starrett, R.A., Decker, J.T., Araujo, A., &Walters, G. (1989). The Cuban elderly andtheir service use. The Journal of Applied Gerontology, 8, 69-85.

Starrett, R.A., Todd, A.M., & DeLeon, L. (1989). A comparison of the social serviceutilization behavior of the Cuban and Puerto Rican elderly. Hispanic Journal ofBehavioral Sciences, 11, 341-353.

Wolinsky, F. (1990). Health and health behavior among elderly Americans. NewYork: Springer.

Dow

nloa

ded

by [

Uni

vers

ity o

f B

ath]

at 0

6:35

09

Oct

ober

201

4

who attends senior centers?

Documents