The
International Journal of Psychosocial Rehabilitation
in Rehabilitation Counseling
Douglas M. Main, Ph.D.
Graduate Coordinator of Counseling Programs
Department of Educational Studies
Eastern New Mexico University
in Rehabilitation Counseling International Journal of Psychosocial Rehabilitation. 12 (1), 37-50
Contact Mailing Address: ENMU, Station 25, Portales, NM (USA) 88130
E-Mail: Douglas.Main@enmu.edu
Predictors of
Time Demand
were investigated as a means of estimating case and caseload difficulty
in
Rehabilitation Counseling. Differences in the time investment of 100
rehabilitation counselors were investigated among a statewide sample of
1330
cases. Criterion time variables were comprised of various groupings of
the
federal status codes prescribed by the United States Rehabilitation
Services
Administration (e.g., planning, counseling, restoration, training, and
placement). Time was thereby partitioned to represent the roles and
functions
of rehabilitation counselors. Using the data reduction facilities of
the SPSS
AnswerTree program (SPSS, 2001), the rehabilitation planning, training
and
placement variables were categorically significant, parametrically
confirmed
and cross-validated. Four case types of
optimal statistical and practical significance resulted. The
rehabilitation
process was reduced to new and existing cases, which were each split
into high
and low service complexity groups. The results of the study have
implications
for quality improvement and organizational development in mental health
and
rehabilitation program management.
Keywords:
Rehabilitation, caseload difficulty, time demand, burnout, workload.
Objective measures of time demand have significant potential for program improvement and research in Federal-State vocational rehabilitation agencies (
Workload measurement was designed to address problems caused by inequities in time demand. In the workload measurement literature it is widely accepted that systemic workload inequity has a negative impact upon organizational effectiveness and efficiency, and upon workforce morale (Kern, 1987, 1988, 1993; Peat, Marwick, & Mitchell, 1978a, 1978b; USDHEW, 1972, 1978; Walls & Moriarty, 1977). The underlying assumption is that in controlling these imbalances, objective workload measurement techniques are superior to subjective workload estimation.
Workload management problems develop when factors that impact time demand differently are not adequately understood or accounted for (Ebb, 1994; Tarvydas and Peterson, 1999). Problems associated with unbalanced workloads include: low morale due to overwork, confusion as to the nature and extent of the work expected, arbitrary and subjective case assignments, increased need for detailed supervisory intervention, and agency difficulty in justifying the need for additional staff and funding (Haring, 1974). Exploring the nature of time demand in the rehabilitation process is needed.This study addressed several issues of importance. Theoretically, the categories of the dependent time variable that best represent the primary roles and functions of the rehabilitation counselor were investigated. Criterion time categories correspond to the functional roles of the work being assessed. According to Kreitner (1995), effective organizational development calls for the ongoing monitoring and adjustment of the role and function of workers. Workload design and redesign are both strongly related to the role and function of the worker as it exists in the present and as it is envisioned in the future. The operationalization of time demand has implications, then, for understanding, structuring and restructuring the work of rehabilitation counseling. Additionally, identifying optimal ways to group the time variable should improve the feasibility of conducting workload measurement research, and help to identify factors that need to be investigated in the future.
Limitations
compromising
the accurate partitioning of time demand in this study include several
internal
and external threats to validity. Internally, selection and
instrumentation
require consideration (Campbell & Stanley, 1963; Cook &
Campbell, 1979;
Gall, Borg & Gall, 1996). Although the sample was not randomly
selected,
the systematic selection of every 12th case on each caseload
approximated random selection, and sample size (N=1330) was adequate.
Regarding
instrumentation, the accuracy of the time log used for data collection
was enhanced
through multiple significance testing and it was cross-validated (SPSS,
2001).
The time log was self-report, however, and susceptible to data
recording error
such as
The purpose of the study was to identify the strongest available predictor of time demand and use it to partition the dependent time variable into comparative and optimally cross-validated models. The criterion time variable was comprised of counselor time investment in minutes reported within 14 functional service categories. The 14 service categories represented the essential roles and functions of the rehabilitation process as formerly developed and mandated through the Rehabilitation Services Administration’s Federal Status Codes. The Federal Status Codes were used to operationalize the first two of six predictor variables (i.e., rehabilitation process levels, case movement, service complexity, caseload size, percent of active cases, and disability type) selected according to their potential for predicting time demand. The null hypothesis was used to test each model.
Literature ReviewSeveral bodies of research relate to counselor time utilization. These include workload management, work role and function, caseload difficulty, and burnout. Of these, the workload management literature is the only one that directly postulates the pivotal nature of time demand, however.
Workload Management
In workload management theory, demoralization occurs when workers face increased time demand on factors that are unrecognized or unaccounted for (Haring, 1974). Conversely, morale and productivity increase as time demand is perceived as fair and controlled (Kern, 1987, 1988, 1993; Peat et al., 1978a, 1978b). The general practice of accounting for time demand is rooted in systems theory. The basic contention is that workers can be easily penalized for systemic problems they cannot control (Lubeck & Davis, 1991), especially when individual production goals compete with team-oriented quality improvement targets (Glaser, 1993). The role and function of work is not a direct aspect of workload management theory, but criterion time categories do correspond to the key functions of the work being assessed (Kern, 1993; Peat et al., 1978b). Role and Function
There is little indication in the role and function literature that time demand is considered an aspect of this construct. In organizational development, however, time is seen as integral to the structure and design of work. According to Kreitner (1995), effective organizational development calls for the ongoing monitoring and adjustment of the role and function of workers. Workload design and redesign are both strongly related to the role and function of the worker as it exists in the present and as it is envisioned in the future.
Time demand is inferred in the rehabilitation role and function literature as well. The Federal Status Codes, developed and initially mandated by the Rehabilitation Services Administration, strongly reflect both time demand and the roles and functions of the profession. These groupings, in order, are: referral, intake, eligibility determination, plan development, counseling services, restoration services, training services, job placement, employment, successful closure, unsuccessful closure after plan development, unsuccessful closure before plan development, and post-employment. Although counselor role and function has been as much a matter of legislated philosophy as of research, some functions are more preeminent and have endured both inquiry and legislative change. Planning, for example, may be the most integral. Although the name of the Individualized Written Rehabilitation Program was recently changed to the Individualized Plan for Employment in the Workforce Investment Act of 1998 (National Governor’s Association, 1998), the planning function remains largely unchanged as the centerpiece of the vocational rehabilitation process.
Recent developments in the rehabilitation role and function literature have been centered in the emergence of counselor specializations (Goodwin, 1992). Counselor specialties often effect changes in the structure of work in the organization. Due to the complexity of the field, counselors are increasingly working with specialty caseloads rooted in either the counselor functions themselves (e.g., job placement services) or in serving specific disability groups (Roessler & Rubin, 1995).
Support in the rehabilitation literature for time demand as an aspect of the role and function of rehabilitation counseling is admittedly indirect and scant (Goodwin, 1992; National Governor’s Association, 1998; Roessler & Rubin, 1995). This report on the Main (2002) doctoral dissertation is the first study to directly link the two. When references to time or time demand are found in rehabilitation research, it is most often in the context of caseload difficulty.
Caseload DifficultyCaseload difficulty research made an early contribution to the emergence of the present priority within public VR agencies of serving persons with most severe disabilities (Miller et al., 1965). Several tools were developed in the late 1960’s to give counselors more credit for working with people with more severe disabilities. Closure weights were developed as proportions of unsuccessful to either successful or total case closures and used as difficulty norms for various client groups (Noble, 1973). Weights were used to add additional numeric value to successful rehabilitations (status 26 closures) from more difficult client types. Correlations between weighted closures and simple case counts were between .94 and .97, indicating a minimal benefit from the use of weightings, however. Calls for more comprehensive caseload difficulty taxonomies, including measures of time demand, went largely unheeded (Wallis & Bozarth, 1971; Walls & Moriarty, 1977).
Due to the difficulty and expense involved in implementing weighted closures, Walls and Moriarty (1977) developed an alternative in which a flexible variety of performance measures could be profiled as percentages or stanines in comparison to national, state or area norms. The average time that clients were staying in a particular status (e.g., 02-10) was suggested as an example of possible time measures that could be included in counselor outcome profiles.
Zadny and James (1977) asked 319 counselors in seven states to estimate their time spent in travel, placement-related travel, and nine case process measures. These were counseling, paperwork, coordinating services, placement, job development, public relations, planning, and professional growth. Time estimates were correlated with two sets of factor analyzed measures representing six outcome dimensions. Total rehabilitations, rehabilitations of persons with severe disabilities, and the percentage of cases closed as not rehabilitated comprised the number of successful closures factor. The nature of successful closures factor was comprised of the percent of all closures that were competitive, the percent of closures in sheltered workshops, and average earnings at closure.
There were no significant correlations in the nature of closures factor, but several relationships in the number of closures factor were significant at the .01 level. The percent of cases closed not rehabilitated was inversely related to time spent in placement (r = -.22), job development (r = -.27), hours spent out of the office (r = -.31), miles traveled per week (r = -.31), travel time spent in job development (r = -.38), and travel time spent in placement (r = -.39). Rehabilitations for clients with severe disabilities were related to time spent in job development (r = .20) and planning (r = .22). Total rehabilitations were inversely related to time spent in coordinating services (r = -.20) and planning (r = -.23). The investment of time in placement related activities was recommended. (Zadny & James, 1977)
McAnally and
Chan, Rosen, Wong and Kaplan (1993) used discriminant analysis to differentiate a group of 18 students from 18 experienced counselors on the basis of caseload process measures (p<. 001). A computer-based multiple case management simulation was developed to measure six task groupings. Three caseload measures were significant (total number of status changes, index of preferred actions, index of inappropriate status changes) and three measures were not significant (money spent on clients, number of actions taken, appropriate closures). Using the classification function, 89% of the participants were accurately classified. This study approximates the use of time standards because participants were given one hour to process eight case types.
Related to caseload difficulty, concern over stress in rehabilitation counseling has also been more recently voiced. Professional disengagement, compassion fatigue, and burnout, have been acknowledged (Greenwood, 1995; Roessler & Rubin, 1995), and counselors have been cited as experiencing disempowerment in the effort to empower persons with disabilities (Emener, 1993). Most importantly, organizational stressors such as role overload (e.g., caseloads that are too large or too active) have been clearly outlined in rehabilitation stress management models (Wood, 1999), and the impact of the organizational system on counselor-client ratios has been given ethical consideration. Tarvydas & Peterson (1999) directly cite the demoralization that occurs for some counselors when counselor-client ratios are not kept consistent with the complexity of the caseload management system and the difficulty of the client population served. Clearly, workload management has potential for addressing these issues.
Interpretive Summary and VariablesThe criterion time variable and the process level predictor of this study are both supported in the rehabilitation literature. The work of Zadny and James (1977) revealed that time spent in placement and job development out of the office as well as time invested in coordinating services in the office contributed to effectiveness. This held true for persons with severe disabilities as well. The administration of rehabilitation workloads, such as decisions related to specialty caseloads (Goodwin, 1992; Roessler & Rubin, 1995), would be facilitated by the ongoing identification of discrepancies between the expected and actual structure of time demand.
The most
notable factor
that is not directly operationalized in the present study is severity
of
disability. The dichotomous designation of severity of disability
required by
the Rehabilitation Services Administration did not evidence enough
variability
on the criterion to be of use in this study. This omission requires
deliberation because relationships between severity of disability and
other
factors have been previously demonstrated (General Accounting Office,
1991;
Zadny & James, 1977). Upon first consideration it appears that this
study
does not contain a measure of severity of disability with an adequate
spread of
scores or categories to demonstrate that time demand is related to the
number
or magnitude of functional limitations created by the disability. State
agencies were first mandated to focus their efforts on serving persons
with severe
disabilities in the Rehabilitation Act of 1973. The presumption within
the
field then and now appears to be that that time demand is unrelated to
severity
of disability and that caseload difficulty is a function of severity of
disability and disability type (
The possibility that time demand is independently or negatively related to severity of disability must be considered, however. Specialty services may have become so well organized around disability groups with most severe disabilities that the actual time needed to serve a person in this category is less than might be expected. Similarly, persons with severe disabilities who are fairly well established in their careers may need only one or two VR services. In essence, time demand itself is the primary mediator of severity of disability to the counselor. On this basis the criterion variable is taken also as an indication of severity of disability.
Six
independent variables
were used as predictors of time demand (i.e., rehabilitation process
levels, case
movement, service complexity, caseload size, percent active, and
disability
type). Case movement is informed by the work of Chan et al. (1993) who
obtained
an indication that when time is held constant, different counselor
experience levels
and rates of task processing (e.g., number of status changes, number of
preferred actions, and number of inappropriate status changes) emerge.
The
independent variables service complexity, caseload size and percent
active
appear to be substantiated by the professional judgment of the
rehabilitation
counselors surveyed by McAnally and
The long history of support for accounting for disability type in rehabilitation research is particularly clear from the studies on caseload difficulty in rehabilitation (Cooper & Pearce, 1980; Miller & Barillas, 1967; Miller et al., 1965; Noble, 1973; Sermon, 1972; Silver, 1969). There is definitely an awareness reflected in the literature that different case types vary dramatically in their difficulty level. There is some indication (Wallis & Bozarth, 1971; Walls & Moriarty, 1977) that successful and unsuccessful clients do not necessarily differ on several matched factors (i.e., age, gender, education, referral source and disability type). This leaves open the possibility that factors related to time demand might account for some of this difference.
The finding
that no case
differences existed between weighted and unweighted cases (Silver,
1969; Wallis
& Bozarth, 1971; Walls & Moriarty, 1977) suggests that case
difficulty
or workload may be similar when time demand is not accounted for. The
specific
recommendation for the use of time-in-status as a performance measure
by Walls
and Moriarty (1977) raises two issues. The recommendation directly
calls for a
real time measure, and it substantiates the need to control for the
effects of
time demand in performance appraisal to avoid biased penalties and
rewards.
This appears to apply to estimated time spent serving persons of a
specific
disability type (McAnally & Linz, 1988), and to production goals
set on the
basis of successful closure rates (Cooper & Pearce, 1980). Efforts
to give
added weight to more difficult closures based on disability type were
short
lived, possibly due to the fact that time demand was not taken into
consideration (
Methodology
The design used in this study was a status survey, quasi-experimental comparison group procedure on the dependent variable time demand. Categories of the dependent and independent variables were reduced using categorical data analysis, and the resulting case types were parametrically cross-validated against a random sample of about 50 withheld cases. Subjects were cases active in March and April of 1999 in a Midwestern state rehabilitation agency.
Procedure
The sample consisted of 1330 systematically selected general VR cases. The cases were open at the time of initial data collection in March of 1999. Each 12th case on every caseload was selected and each of 105 counselors was then given a list of the cases chosen for the study. In addition, each counselor selected the first four new referrals received in the data collection period. Of the 1330 cases in the original sample, 993 (74.7%) were classified as having a severe disability. Primary disability groups included 392 with psychological disabilities, 345 orthopedic, 251 with diseases and related conditions, 142 with visual impairments, 98 with hearing impairments, and 39 with a brain injury. Top referral sources were 333 from education, 253 from healthcare, 226 from welfare and social services, and 296 were self-referrals. Twenty-six percent had more than one disability and 16% had more than three services listed on their rehabilitation plans.
Time logs were used to record time investments in each of the process categories for one month. Counselors self-reported their time investments for each case in minutes. Demographic information used to determine client types was also recorded on the time log form. Data was taken directly from the computer information system of the state agency or from the case file. Of 15 variables for which data were collected by the state agency, five were selected and modified as independent variables for the study. No validity or reliability studies of the time log instrument were conducted, but the reliability of the log was largely confirmed through the parametric cross-validation in the analysis.
Secondly, the strongest predictor of time demand was identified through the categorical data reduction of the six predictor variables on the criterion time variable. Categorical data reduction is the term used in the AnswerTree program (SPSS, 2001) to describe the process of running multiple significance tests among competing predictor variables, identifying the most significant predictors and splits in each target variable. The process results in a diagram called a tree in which each category is called a node, and each split is called a branch. When the data is reduced until no further splits are statistically significant, terminal nodes are attained.
Third, the strongest predictor was used to partition the time variable from comparative theoretical and empirical standpoints. The most viable models, finally, were cross-validated against a random sample of approximately 50 withheld cases.
MeasuresThe dependent variable for the study was time reported in minutes. Rehabilitation process levels was the first independent variable, comprised of the 14 federal status codes (i.e., referral, intake, eligibility determination, plan development, counseling services, restoration services, training services, job placement, employment, successful closure, unsuccessful closure after plan development, unsuccessful closure before plan development, and post-employment). These process levels could be theoretically partitioned into groupings that represent various roles and functions of rehabilitation counseling (e.g., planning, counseling, restoration, training, placement, etc.).
The second predictor variable was the dichotomous number of federal status code changes, per case, in the month of data collection, taken as a measure of case movement (one or more than one). This variable gave an indication of the relative speed at which different case types move through the rehabilitation process. The third predictor was the number of specific service categories from the month prior representing service complexity (one or more than one). Fourth, caseload size was dichotomized as small or large (below the median; at or above the median). Fifth, the proportion of cases that demanded time for each counselor was taken as a measure of the percent of active cases (below 50% and 50% and up). Finally, disability type included visual, hearing, orthopedic, mental illness, developmental disabilities, learning disabilities, and all other conditions.
Data AnalysisCategorical data reduction was used to identify the strongest predictor of time demand and to test various partitions of the dependent time variable. Viable partitions were cross-validated parametrically. The Chi-Squared Automatic Interaction Detector (CHAID) methodology within the SPSS Answer Tree program (2001) has parametric and nonparametric facilities, and was used for all data analysis procedures. In the categorical models, the chi-squared maximum likelihood-ratio test of significance was used for each level and branch of the tree. For the parametric models and cross-validation samples, Analysis of Variance tests of significance were used. Probability levels were computed in each branch of the analysis using an appropriate Bonferroni adjustment. Results
Surprisingly, it was not necessary or possible to implement the recommendations of Peat et al. (1978b) that the mode be used for work unit standards, and that extreme scores and case types that do not apply to the whole sample be removed. The mean seemed to be the best summary statistic in most cases because the distribution of time in the sample was inverted, flat, and positively skewed. Mean, median and mode minutes per case were 70, 35 and zero, respectively. No time demand was reported for 304 cases in the original data set, and 312 cases after adjustments, making it infeasible to use the mode as Peat et al. (1978b) suggested.
Furthermore, no outlying cases or caseloads were removed from the analysis. In assessing the impact of outlying scores, the 10 individual cases with the most total minutes reported ranged from 895 to 525. These cases appeared to be valid and proportionately distributed among the disability types. The largest and smallest workloads reported were from blind services caseloads, which limited their extraneous impact. The largest number of minutes reported for a caseload was 2985, or an average of just under four hours spent on each of the 14 cases sampled over the month of data collection. The smallest number of minutes reported for a caseload was zero. This caseload had been recently vacated and assigned to another counselor for triage. None of the extreme cases or caseloads seemed unreasonable and none were adjusted or removed.
Several adjustments were made for missing values, however. Missing values on the total time variable (N = 41) were computed as the sum of all process minutes reported. Missing values on the service complexity predictor demanded only slightly less time than the time demanded by cases having one service category and were combined (i.e., coded less than two, or two or more). Three of 100 caseloads missing the caseload size value were coded at the mean caseload size for their respective caseload types (i.e., blind services, 56; general caseload, 90). Missing disability type values were assigned separate codes within their respective caseload types (i.e., visual, hearing, mental health, general). The adjusted data set included an alternative coding system that made use of available information about each missing value. Potentially, the full data set (N = 1330) could be used for some analyses, and missing values could be selectively removed.
The full data set was used to identify the strongest predictor of time demand categorically, as recommended by Peat et al. (1978b). The model was nonparametric and automatic, meaning that the insignificant levels of the variables could all be merged or reduced.The rehabilitation process levels entered the model first (Chi-square = 215.3811; p = .0000) and the levels were reduced to three groups. Planning was merged with new cases. Service categories 14, 16, and 18 (i.e., counseling, restoration, training) were merged with existing cases, and the placement category remained a single group. The strongest predictors, in order, were process levels, percent active, service complexity, and case movement (Chi-square > 4.8190; p < 0.0282).
Cross-validation, however, fell short of the parametric capacity of the AnswerTree program. Parametric confirmation had to be customized. In other words, the parametric version of the AnswerTree program identified more accurate group splits that were not used in this reduced, categorical model. Two terminal case movement nodes on the high complexity, new cases branch were not confirmed (F = .6011; p = .2750), and multiple interactions were found in the cross-validation sample (N = 68) that did not exist in the confirmation sample (N = 1262). Also, four of the nine, terminal cross-validation sample nodes had less than five cases. Parametric confirmation of the process levels predictor was strong. The null hypothesis was rejected, but a parametric model with stronger splits and branches was clearly indicated for future workload measurement purposes.
Several theoretical models were tested, meaning that different partitions of the process levels predictor were forced into the analysis and could not be reduced. One of the strongest of these was a symmetrical five group model (i.e., planning, counseling, restoration, training and placement). The theoretical rehabilitation process groups better approximate the role and function literature. Cases with missing federal status codes were removed from this model to avoid their extraneous mergers with the theoretical groupings (N = 1175). The five theoretical process categories were each significant when tested using the parametric confirmation facility of the CHAID procedure (p < .0211, F > 5.4135). The null hypothesis was again rejected. Service complexity was the strongest nonparametric predictor of rehabilitation planning, counseling and restoration service groups (Chi-square > 8.4063, p < .0038), and the percent active variable was the strongest predictor of training and placement groups (Chi-square > 17.7934, p < .0001). Six terminal nodes did not cross-validate, however, not improving on the reduced model.
Therefore, an automatic, parametric data reduction was used and cross-validated to more fully tap the practical utility of the AnswerTree program. For this automated model, the modified means were used as the process variable and the process groups were scaled ordinally. Although the nominal scaling of the process categories produced more nodes with more exacting mergers, the ordinal scaling of the groups was simpler, more logically understandable, and more consistent with the theoretical process categories.
Two process levels resulted (F = 202.4067; p = .0000). Cases in status codes 00, 01 and 02 were merged into a new-cases node (i.e., referral, new and applicant statuses, respectively). Cases above status 02 comprised an existing-cases node (i.e., eligible, plan completed, counseling, restoration, training, existing, and placement statuses, respectively).
Service complexity was the strongest predictor for both new and existing cases (F >14.6111; p < .0003). The base standards in Figure 1 are based on the process and complexity nodes of the second level, before the third variable entered the model. In the existing-cases node, percent active was the third and final variable to enter the model (F >19.0323; p = 0.0000).
Each of the
six terminal
nodes cross-validated in the predicted direction. No interactions were
created
in the cross-validation sample (N = 53), and only one node contained
less than
five cases (N = 3). The optimal splitting function of the AnswerTree
program
was strongly implemented, and no reasons were discovered for
customizing the
model. Therefore, the case type means from this automatic model
appeared to be
optimal for the further development of workload standards. Figure 1
displays
these means.
Conclusions
The utility of workload management in rehabilitation was supported in several ways. First, caseload size and disability type were not strong enough predictors to enter the workload model. This suggests that time demand is a mediator of caseload difficulty and severity of disability. Secondly, disability type was not represented in the final model that accounted for 21.17% of the variance in time demand. This suggests some independence between the two variables and lends support to time measurement as superior to the variables typically used to estimate workload (Haring, 1974). Third, multiple significance testing (SPSS, 2001) enhanced the theoretical workload models through parametric confirmation, and provided empirically superior alternative models. Workload measurement procedures were enhanced. Last, the operationalization of roles and functions was supported by the strength of the rehabilitation process, case movement, and service complexity variables. This supports time demand as a mediator of the roles and functions of rehabilitation counselors (Goodwin, 1992; Kreitner, 1995; Roessler & Rubin, 1995).
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