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Constructive conflict and staff consensus in substance abuse treatment
Journal of Substance Abuse Treatment 36 (2009) 174 – 182
Regular article
Constructive conflict and staff consensus in substance abuse treatment Gerald Melnick, (Ph.D.)⁎, Harry K. Wexler, (Ph.D.), Michael Chaple, (M.A.), Charles M. Cleland, (Ph.D.) Center for the Integration of Research and Practice (CIRP), National Development and Research Institutes, Inc., (NDRI), New York, NY 10010, USA Received 20 December 2007; received in revised form 9 April 2008; accepted 5 May 2008
Abstract Previous studies demonstrated the relationship between consensus among both staff and clients with client engagement in treatment and between client consensus and 1-year treatment outcomes. The present article explores the correlates of staff consensus, defined as the level of agreement among staff as to the importance of treatment activities in their program, using a national sample of 80 residential substance abuse treatment programs. Constructive conflict resolution had the largest effect on consensus. Low client-to-staff ratios, staff education, and staff experience in substance abuse treatment were also significantly related to consensus. Frequency of training, an expected correlate of consensus, was negatively associated with consensus, whereas frequency of supervision was not a significant correlate. The implications of the findings for future research and program improvement are discussed. © 2009 Published by Elsevier Inc. Keywords: Constructive conflict; Staff consensus; Organizational culture
1. Introduction 1.1. Prior findings Melnick, Wexler, Chaple, and Banks (2006) have examined the effects of a cohesive organizational culture as defined by consensus on the importance of various therapeutic community (TC), cognitive behavioral, and 12-step activities within substance abuse treatment programs. Two types of agreement were explored, consensus, representing agreement within groups (e.g., primary substance abuse treatment counselors or clients), and concordance, representing agreement between groups (e.g., counselors and clients). In all cases, high levels of agreement, both within and between groups, were associated with higher levels of client engagement in treatment (Melnick et al., 2006). In an additional test of the effect of consensus on treatment ⁎ Corresponding author. Center for the Integration of Research and Practice (CIRP), National Development and Research Institutes, Inc. (NDRI, 71 W 23 Street, 8th Floor, New York, NY 10010, USA. Tel.: +1 212 845 4400; fax: +1 212 845 4650. E-mail address: [email protected] (G. Melnick). 0740-5472/08/$ – see front matter © 2009 Published by Elsevier Inc. doi:10.1016/j.jsat.2008.05.002
outcomes, an analysis of the Drug Abuse Treatment Outcomes Study showed that high levels of client consensus in beliefs about the role of abstinence in recovery at 1 month in treatment predicted drug and alcohol use at 1 year posttreatment follow-up (Melnick, Wexler, & Cleland, 2008). 1.2. Potential implications for treatment programs Organizational culture has been defined as the shared beliefs of an organization's members (Ostroff, Kinicki, & Tamkins, 2003; Schein, 1996, 2004) that determine the attitudes, perceptions, goals, and ultimately, the behavior of its members (Schein, 1996). High levels of agreement are believed to reduce the amount of uncertainty within an organization (Galbraith, 1977) and to engender consistency and predictability in its members' reactions to events, resulting in reduced friction and improved member relationships (Zander, 1994). In a less-cohesive culture, values are vague or ambiguous, which leads to differing interpretations, inconsistent actions, and conflict between members or groups of members (Martin, 2002; Martin & Meyerson, 1988). Thus, in a cohesive culture, core values are consistent throughout the organization and help the
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organization to integrate its various activities and adapt to change (Schein, 2004). A cohesive culture is especially important in health-care-related organizations that depend on the contribution of many different individuals (Tulchinsky & Varavikova, 2000) and in substance abuse treatment programs where trust and cooperation are important to the introduction of new treatment technologies (Lehman, Greener, & Simpson, 2002). Melnick et al. (2006, 2008) have defined consensus as a special case of organizational culture referring to the level of agreement concerning beliefs related to treatment goals and protocols. In substance abuse treatment programs, consensus may involve the goals of treatment, such as total abstinence being the way to recovery, and important aspects of the treatment protocol, such as the use of work as therapy, the use of thought-stopping techniques, or the belief in the importance of recognizing a higher power. When the level of consensus is high, all staff members will teach the same goals and will react in a similar manner, transmitting a consistent message and a clear set of expectations that serve to facilitate the therapeutic process, culminating in higher levels of client engagement and better treatment outcomes. 1.3. Need to understand the antecedents of consensus The study reported here seeks to fill a gap in our understanding of the correlates of consensus in substance abuse treatment programs. Previous studies provided empirical support for the relationship between organizational cohesion, as represented by consensus and concordance, and the performance of substance abuse treatment programs and referred to a well-developed theory within the area of organizational development (Melnick et al., 2006, 2008). These studies, however, have yet to identify the variables responsible for the development of consensus that would permit the experimental manipulation of consensus and inform treatment programs of the relevant organizational interventions. The focus of this study is on staff because the implicit model of the internal effects within programs suggests that the transmission of core values to the clients is heavily influenced by the more permanent members of the community (the primary substance abuse treatment counselors), who will transmit these beliefs to the new members (clients) so that consensus among counselors will lead to more consensus among clients and concordance between counselors and clients. This model follows the conception of culture as a pattern of shared basic ideas that have been successful enough to be considered valid and that are taught to new members as the proper way to approach and solve problems (Schein, 2004). Possible antecedents of consensus have not been collectively compiled, but prior theory and research point to communication as a critical factor. For example, downward communication predominates in many organizations. Under these conditions, supervisors frequently overestimate
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the amount of information that subordinates possess (Likert, 1961), as well as how often and how clearly they have communicated information to subordinates (Callan, 1993). Decentralized communication, consisting of vertical and horizontal patterns of interchange (Clampitt & Downs, 1993; Tschan, 1995), maximizes the sharing of information, is self-correcting for the meaning of the communication, and would be expected to affect the amount of information that counselors share about the treatment process. Program policies regarding open planning and decision-making processes (D'Aunno, FozMurphy, & Lin, 1999; Roman & Johnson, 2002) may also be expected to influence staff consensus. For example, people are more likely to invest in and to commit to policies that they have helped to shape than to policies that were imposed upon them (Cotton, 1995; Sagie, 1995; Wanberg & Banas, 2000). Even when not directly involved in the decision making itself, people are more willing to accept administrator-derived policies if their input has been considered in the actual implementation (Sagie, Elizur, & Koslowsky, 1990, 1995). Conflict resolution is another factor that might be important to building staff consensus. Participation will almost always generate some degree of conflict as staff members with different attitudes or perspectives express their opinions. Organizations with norms for expressing and dealing with conflict will be able to resolve differences and achieve higher levels of cohesion than organizations that either suppress differences or are unable to reach a constructive resolution (Jehn, 1997; Kirchmeyer & Cohen, 1992; Parker, 1993). Therefore, tolerance of disagreement and conflict resolution skills are expected to foster consensus. Several static variables might be expected to affect organizational cohesion. For example, size is related to organizational structure, so that smaller programs are less bureaucratic than larger ones, which may enhance and increase opportunities for the entire staff to interact with one another (Alexander et al., 1997; Moos, 1997). Particularly germane to the present study, program size has been related to variations in program practices (Jonkman, McCarty, Harwood, Normand, & Caspi, 2005). Staff tenure (Knudsen, Johnson, & Roman, 2003; Moos, 1997) affects the opportunity for communication and consensus building over time. Low staff–client ratios (Prendergast, Podus, & Chang, 2000; Welsh, 2000) are associated with more opportunities for staff interaction and with more staff contact per client (Welsh, 2000). In addition, staff training, supervision, experience, and education all contribute to a shared understanding of the program's goals and treatment approach (D'Aunno & Vaughn, 1995; De Leon, 2000; Knudson, Ducharme, & Roman, 2006); consequently, higher levels of each of these are expected to raise levels of consensus among the staff. The study reported here attempts to extend previous findings by identifying factors associated with a cohesive organizational culture characterized by high levels of
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consensus. Because consensus has been shown to influence both treatment process and outcomes, identifying the correlates of consensus will facilitate the formulation of a causal model that will inform the development of interventions to enhance treatment quality.
2. Materials and Methods 2.1. Sampling Sampling and data collection were conducted by the National Opinion Research Corporation of the University of Chicago. Data collection took place between April and September 2004. The sampling frame consisted of the National Directory of Drug and Alcohol Abuse Treatment Programs (Substance Abuse and Mental Health Services Administration [SAMHSA], 2003), which is a listing of federal, state, and local government as well as private facilities that provide substance abuse treatment services. These facilities (a) were licensed, certified, or otherwise approved for inclusion in the directory by their state substance abuse agencies and (b) had responded to the 2002 National Survey of Substance Abuse Treatment Services, which SAMHSA had been conducting annually. The sample was restricted to programs that (a) had been in operation (seeing clients) for a minimum of 3 years; (b) served an adult population; (c) had a minimum planned duration of stay of at least 60 days; and (d) were freestanding (not part of a hospital, school, or university). A random sampling procedure with replacement was used to establish a sample of 80 residential treatment programs. The sample was stratified by program size, with 20 programs included from each of four bed-size categories (i.e., 15–25, 26–50, 51–100, and N100 beds) to prevent bias toward any particular program capacity. To ensure a geographically diverse sample, we selected programs from metropolitan statistical areas (defined by the U.S. Census Bureau) in New York, New Jersey, Massachusetts, New Hampshire, Illinois, Indiana, Wisconsin, Texas, California, and Arizona. A total of 118 programs were determined to be eligible and approached to participate in the study; 38 of these (32%) refused participation. Each program that volunteered to participate in the study was given $1,000 as compensation for completing the survey. Data were collected for the study using the Multimodality Quality Assurance (MQA) instrument (Melnick & Pearson, 2000), which had been developed, tested, and used previously for group administration (Melnick, Hawke, & Wexler, 2004; Melnick & Wexler, 2004). Survey respondents in each program consisted of primary drug treatment counselors who were on duty at the time data were collected; when data were collected, 86.4% of the primary drug counselors were available (the remaining 13.6% were off-site). Surveys were completed in a group setting and facilitated by a trained interviewer who first
explained the process of informed consent and the purpose of the project and who remained available to answer any questions that counselors had in the course of completing the instruments. Data collection sessions were scheduled during staff meetings so that completing the instrument was not perceived as an additional encumbrance and did not interfere with therapeutic activities. Generally, the staff needed 20 to 30 minutes to complete the instrument. Of the 595 staff across the 80 programs, 7 (1%) either refused or were otherwise unable to complete the survey. The lowest response rate of the staff on duty at the time of testing at any program was 78%; 2 other programs included a single staff member who refused to participate. Overall, an average of 86% of program staff participated in study. Table 1 shows the characteristics of the programs included in the study. Most programs (65%) were described by the administrator as TCs. Programs were well established, admitting clients for a median of 22 years. The median enrollment (i.e., measured over the most recent 12-month period) was 70 clients, and the median ratio of primary drug counselors to clients was 1:12. Table 2 shows the characteristics of primary drug treatment staff employed at these programs. Most of the staff (68%) had at least some college education, most (59%) had substance abuse treatment training outside the program, and just under half (49%) had substance abuse treatment credentials. More than three quarters (77%) of the staff had 3 or more years of experience in treating substance abuse, and most (61%) had been employed at their present program for more than 2 years.
Table 1 Program characteristics Characteristics Program orientation (%) TC Cognitive–Behavioral therapy Mutual self-help/12-step Other Length of time (in years) admitting clients (median) Average enrollment in the past 12 months (median) Ratio of clients to drug counselors (median) Ratio of clients to all counselors (median) Frequency of group staff meetings (mean) Frequency of individual staff meetings (mean) Client demographics (%) Proportion of male clients (median) Proportion of Hispanic clients (median) Proportion of Black clients (median) Proportion of White clients (median) Staff demographics (%) Proportion of male staff (median) Proportion of Hispanic staff (median) Proportion of Black staff (median) Proportion of White staff (median)
Residential programs sampled (n = 80) 65 7 20 8 22 70 12.07 3.82 2.74 (0.83) 3.31 (1.2) 77 21 30 47 50 11 33 52
G. Melnick et al. / Journal of Substance Abuse Treatment 36 (2009) 174–182 Table 2 Staff characteristics Characteristics Staff education (%) Lower than high school High school or GED Some college Associate's degree Bachelor's degree Some graduate school Staff credentials (%) Substance abuse training outside the program Substance abuse credentials Mental health credentials Years working in substance abuse treatment field (%) b1 year 1–2 years 3–5 years N5 years Staff tenure: length of employment in program (%) b6 months 6 months–1 year 1–2 years N2 years Staff receiving in-service training (%)
Residential programs sampled (n = 80) 4 28 28 17 16 7 59 49 9
8 15 27 50 15 9 15 61 31
2.2. Measures 2.2.1. Treatment implementation Measures of treatment implementation consisted of three scales from the MQA instrument (Melnick & Pearson, 2000; Melnick & Wexler, 2004; Melnick et al., 2004) that examine TC, cognitive behavioral treatment (CBT), and 12step treatment elements, three of the most frequently used residential treatment modalities (Lipton, Parson, & Wexler, 1999). Each scale consists of 15 Likert-type items in which statements are rated from 0 (not at all important in our program) to 3 (extremely important in our program). Some examples for items from each modality include the following: “clients confront unacceptable behavior outside individual and group counseling” (TC); “[program] explains the use of thought-stopping techniques” (CBT); and “[program] emphasizes the need to rely on a higher power” (12-step). A previous factor analysis found the scales were unidimensional with good reliability (Melnick et al., 2004). This study followed the recommendation previously reported (Melnick et al., 2004, 2006), combining the TC, CBT, and 12-step treatment scales to achieve a better assessment of the overall treatment experience. This approach was supported by an analysis of variance of staff mean scores for the three scales, which found no difference between mean scores in programs described by their directors as TC, CBT, or 12-step. In addition, the overall coefficient of reliability (Cronbach's alpha) scale reliability for the combined measure was .94 for staff; this high alpha coefficient was equivalent to the alpha
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coefficients for the individual TC, CBT, and 12-step treatment scales, which ranged from .82 to .95, a finding that further supported combining scales into a single measure of treatment implementation. 2.2.2. Organizational cultural norms for communication Program communication was investigated as a reflection of the organizational culture insofar as communication represents the organization's core beliefs and norms regarding the expected interactions of its members. Specifically, program communication is defined by three unique scales that explore multilevel communication, planning, and decision making and conflict resolution. The multilevel communication scale is composed of seven similar questions that examine interpersonal lines of communication between program members to determine the direction of the communication flow across organizational levels within the program. The seven items were constructed using the logic of sociometry (see, for example, Newcomb, Bukowski, & Pattee, 1993), which, for this study, examines interrelationships within the larger program structure. An example of an item on this scale is “Program director starts discussions with counselors regarding problems or concerns about the program.” For each statement, counselors were asked to indicate the frequency of occurrence in their program, with responses ranging from 0 (never) to 3 (often). The alpha reliability for this seven-item scale is .78. The planning and decision-making scale was adapted from the Staff Decision-Making Scale (Parker, 1993; Tannenbaum, Kaycic, Rosner, Vianello, & Weiser, 1974) and is designed to measure staff feedback, specifically the extent to which supervisory staff and program directors solicit opinions and suggestions from counselors when making decisions about program policies and/or treatment issues. An example of an item on this scale is “How often does the program director ask for counselor's opinions and suggestions about treatment issues?” The total scale consists of six items that ask respondents to indicate the frequency of occurrence in their program, with responses ranging from 0 (never) to 3 (often). The alpha reliability for this six-item scale is .86. The nine-item constructive conflict resolution scale was adapted from previous work on organizational conflict (Kirchmeyer & Cohen, 1992). The nine items measure the extent to which staff express and resolve task-oriented conflicts in a productive and constructive manner. An example of an item on this scale is “We have open and frank discussions about our differences.” Counselors were asked to indicate the extent to which each statement is true, that is, “not true,” “somewhat true,” mostly true,” or “very true.” Three negative statements requiring reverse scoring (e.g., “people are afraid to speak up for fear of ridicule or retaliation”) were removed to eliminate the negative impact of the method variance (Doty & Glick, 1998; Malhotra, Kin, & Patil, 2006) on scale reliability. Indeed, the alpha
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reliability of .64 for the nine-item scale improved to .87 when three negatively phrased items were removed. 2.2.3. Formal program activities expected to affect consensus Direct measures expected to influence consensus included the number of training sessions in the last year and the frequency of group and individual supervisory meetings in which clients were discussed (e.g., daily, two or three times a week, weekly, monthly, less frequently than once a month). 2.2.4. Program and staff characteristics that may indirectly affect consensus Indirect measures that might affect consensus included education level (ranging from no high school diploma or GED [General Educational Development] to graduate school education); staff experience (number of years spent working as a substance abuse counselor); and primary treatment counselor-to-client ratios (numbers of primary treatment counselors and current clients, which reflect staff time available to reflect or communicate about treatment activities). 2.3. Analytic strategy 2.3.1. Calculating consensus on treatment implementation The measurement of consensus was derived from the agreement among staff regarding the level of treatment implementation in their respective programs. A mean score was aggregated to the program level, and the measure of consensus was determined primarily from the standard deviation from that mean score. The precise calculation of consensus follows prior work (Melnick et al., 2008) and includes a minor modification to an earlier formula (Melnick et al., 2006). In this calculation, the within-group variance on “treatment implementation” is indicated by a score that tracks positively with consensus and that can range from 0 (no consensus) to 1 (perfect consensus). Because larger standard deviations equate to less agreement, the measure is reversed so that larger scores represent greater agreement. For each program, the maximum possible standard deviation was determined; that is, the standard deviation that would be observed if responses were split evenly between the highest and lowest points on the scale. For example, if 20 staff in a program were to respond, the maximum possible standard deviation would be observed if 10 of those staff were to respond with 0 and the other 10 with 3 (SD = 1.539). The observed standard deviation in the program was divided by this number, with the result subtracted from 1. Thus, the formula for calculating consensus can be expressed as: 1
observed SD maximum possible SD
The correlation between the derived ratio and the observed standard deviation was 0.99. Although the maximum possible SD is program specific, the introduction of the ratio simplifies comparisons between different programs and studies.
2.3.2. Data analysis In recognition of the special role of the mean score in determining consensus, partial correlations, controlling for the consensus mean sore, were used to determine the association between the separate measures and staff consensus as to the importance of the various TC, CBT, and 12-step treatment activities in the substance abuse treatment program. Multiple regression was then used to examine the unique association of each independent variable with consensus. Variables entered in the first of two blocks included (a) the mean score of the treatment implementation scale; (b) the projected indirect program and staff influences on consensus; and (c) the projected direct program influences on consensus. The three variables that characterized organizational cultural norms regarding communication within the program were entered in the second block. These variables were entered in a separate block because they were conceptually related to each other and qualitatively different from the other independent variables that captured more structural and less dynamic aspects of programs. Grouping the organizational cultural norms regarding communication together also allowed for examination of the association of those variables as a group with consensus. The mean score signifying the level at which treatment activities were considered important was included as a control variable because consensus is reduced when the mean approaches its floor or ceiling. 3. Results As expected, the correlation between the mean score of the treatment implementation scale and consensus on this same scale was relatively large and significant (r = 0.568, p b .001). Table 3 examines the partial correlations between the possible correlates in the study and consensus after controlling for the effects of the treatment implementation mean score. Only the
Table 3 Partial correlations between main study variables and staff consensus controlling for mean treatment implementation scores Staff consensus Staff tenure Program size a Client: drug counselors Staff experience level Staff education level Staff training level Frequency of individual staff meetings Frequency of group staff meetings In service training Multilevel communication Planning and decision making Conflict expression/resolution
.209 .003 .228 .032 .088 −.288 ⁎ −.085 −.038 −.058 .031 .209 .374 ⁎⁎
a Average number of participants enrolled during past 12 months. ⁎ Significant at b.05. ⁎⁎ Significant at b.001.
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frequency of staff training and constructive conflict resolution were significantly correlated with consensus, with training showing a negative correlation. 3.1. Effects of organizational cultural norms regarding communication on staff consensus Table 4 shows the collective impact of key program characteristics on staff consensus regarding the level of treatment implementation. The first block of independent variables accounted for 45% of the variance in consensus (p b .01). Prior to the inclusion of the communication/ organizational culture variables in the second block, only the mean treatment implementation and the staff training level were significant, with the latter showing a negative relationship to consensus. The addition of the variables in the second block accounted for a significant increment in the variance in consensus (ΔR2 = 0.216, p b .001). With all variables in the model, the ratio of drug counselors to clients, staff education, training, and staff experience each demonstrated a positive relationship with staff consensus. Controlling for all other variables, constructive conflict resolution emerged as the strongest predictor of staff consensus.
4. Discussion Conflict resolution had the largest unique association with staff consensus regarding the belief in the importance of various treatment activities. Table 4 introduces the communication scales as a block; additional analyses (not shown) investigated the effect of adding the communication variables singly to the model, but no significant relation with consensus emerged, whereas the planning and decision making scale was significant when added by itself to the model, as was the constructive conflict
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resolution scale. The correlation between these latter two scales was 0.71, indicating a strong association between programs that actively engage their staff in planning and decision making and the ability of the program to deal with conflict constructively. Staff education and experience were both associated with consensus, but only in the presence of organizational norms for communication. This suggests that when a program has a culture with norms for conflict resolution that permit dissent to be voiced and resolved, the experience and education of its staff might be better integrated or otherwise utilized. A similar effect was noted for staff-to-client ratios, where low ratios were associated with high staff consensus only after the block of communication variables was added. This provided an additional instance in which the cultural variables related to communication, and conflict resolution appeared to have facilitated the association between other program or staff characteristics and consensus. Activities expected to have a direct effect on staff forming a common set of beliefs about treatment, such as training and supervision, were either neutral or negative in their relationship with consensus, which remains to be explained. It may be that high levels of training or supervision are the programs' response to low levels of consensus within their staffs. In addition, if some staff members are trained and some are not, in the absence of discussion and conflict resolution, different perspectives could emerge and undermine consensus. The question remains as to how organizational cultural factors related to communication affect consensus. Conflict is generally considered to be disadvantageous to an organization in achieving its goals but can serve to the benefit of organizations when organizational norms for expressing and resolving conflict are present (Aritzeta, Ayestaran, & Swailes, 2005; Desivilya & Yagil, 2005). Under this latter condition, conflict can lead to innovation at
Table 4 Staff ratings of communication scales predicting consensus (controlling for informal and formal measures) Model I b Block I—program and staff characteristics Staff treatment mean score .195 Staff tenure −.005 −.000 Program size a Client: drug counselor .001 Staff experience level .110 Staff education level .137 Staff training level −.167 Frequency of individual meetings −.018 Frequency of group meetings .016 In service training −.001 Block II—organizational norms for communication Communication Planning and decision Constructive conflict resolution a
Model II B
t/f
p
b
B
t/f
p
.433 −.013 −.123 .236 .196 .211 −.274 −.152 .069 −.035
2.87 −.090 −.669 1.40 1.40 1.54 −2.33 −1.19 .758 −.747
.007 .929 .508 .171 .169 .133 .026 .243 .453 .460
.101 −.042 .000 .003 .163 .176 −.162 −.009 .011 .000
.224 −.113 −.293 .505 .290 .271 −.320 −.089 .076 −.041
1.65 −.924 −1.81 2.72 2.12 2.40 −2.72 −.699 .602 −.371
.109 .363 .080 .010 .042 .022 .010 .489 .552 .713
−.015 −.044 .191
−.043 −.182 .685
−.349 −.848 3.26
.729 .403 .003
Measured by average number of participants enrolled during past 12 months.
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the organizational level and staff growth (Cloke, Goldsmith, & Bennis, 2005; Runde & Flanagan, 2007). To some extent, the effects of conflict depend on its perception or on the beliefs regarding the nature of conflict within the culture of the organization. Conflicts within organizations can be perceived as either affective, focusing on the individuals or groups who attain power, or substantive, focusing on goals (e.g., abstinence or moderate use of drugs in recovery) or the means of achieving goals (e.g., the use of work as a therapeutic activity). Regardless of the type of conflict, people tend to interpret dissent affectively as personal attacks rather than as substantive differences (Ginzel, 1994; Jehn, 1997). Constructive communication norms can neutralize this tendency by influencing the interpretation of disagreements within the organization to favor substantive rather than affective conclusions (Bottger & Yetton, 1988; Jehn, 1995; Schweiger & Sandberg, 1991). Where differences are perceived as substantive, organization members feel free to express disagreement and believe their opinions are respected (Jehn, 1995). Moderate levels of conflict can be constructive, stimulating wider discussion of ideas and resolving differences between members, ultimately building consensus. In this manner, organizations that permit dissent and have constructive methods for dealing with disagreements will be able to reach more lasting consensus than those organizations that either suppress differences to avoid conflict or are otherwise unable to achieve resolution (Graham & Keeley, 1992; Parker, 1993). In the context of this study, norms that favor conflict resolution might encourage more experienced or educated staff to contribute to the program and could provide a mechanism for identifying and reducing differences. Conflict resolution would appear to play an important role in the delivery of treatment and the adoption of new technology, as consensus would be expected to increase the consistency and longevity with which a treatment protocol is delivered, teaching new staff as part of the informal transmission of culture within the organization (Schein, 2004). In this vein, it is interesting to note the possible connection between constructive conflict and the role of open communication (Coiera, 2003; Kaplan, 1997; Klein, Conn, & Sorra, 2001; Taxman, Cropsey, Melnick, & Perdoni, in press; Wejnert, 2002) and consensus building (Bero et al., 1998) in the adoption, implementation, and fidelity of best practices. The perspective is a dynamic one in which organizations that continuously consider and resolve differences are more likely to reach agreement on ever improving policies, procedures, and processes (Runde & Flanagan, 2007). Such organizations may also be more likely to engage in continuous quality improvement because the effectiveness of clinical interventions would be more likely to be discussed. It is also important that this study takes place in a context in which staff and client consensus about the treatment being implemented has been shown to be associated with client engagement in treatment (Melnick et al., 2006), and
client consensus concerning abstinence as the goal of recovery at 1 month in treatment has been shown to influence 1-year treatment outcomes (Melnick et al., 2008). Thus, we suspect conflict resolution increases staff consensus about the treatment protocol, which in turn increases client engagement in treatment, and that these increased levels of client engagement result in better treatment outcomes. This model has yet to be tested in longitudinal study beginning with the experimental manipulation of consensus by means of an organizational intervention to improve norms for conflict resolution. Although conflict resolution is a strong correlate of staff consensus in the study and offers an attractive theoretical basis for understanding the development of staff consensus, several limiting factors need to be considered. As noted above, although the study reports a strong unique association between conflict resolution and staff consensus with some confidence, evidence for a causal connection awaits the experimental manipulation of organizational norms concerning conflict resolution along with the longitudinal study of the influence of consensus on client engagement and outcomes. 4.1. Limitations This study had some limitations that may affect the generalization of the results. The sample was restricted to long-term residential treatment programs, where staff might interact differently than in outpatient programs; therefore, current findings concerning conflict resolution might be limited to residential programs. Second, although the study represented a geographically diverse group of programs, it was limited to the 68% of the programs contacted that agreed to participate and to the 86% of staff available at the time data were collected (the staff refusal rate was small at only 1%). In addition, the stratified sampling procedure resulted in oversampling the larger programs, so that the effect of these programs is overrepresented in the findings. Furthermore, of the myriad factors that have the potential to affect staff consensus, only a limited number have been considered in this study; other variables might prove to be more closely associated with staff consensus.
5. Summary In summary, conflict resolution was found to be an important correlate of staff consensus regarding the value of various treatment activities and appeared to facilitate the effects of other variables, such as counselor-to-client ratios and staff education and experience, in creating higher levels of staff consensus. These results are promising in pointing the way to the development of interventions that will increase staff consensus and to the formulation of causal relationships among conflict resolution, staff consensus, and program outcomes.
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Schein, E. H. (2004). Organizational culture and leadership, (3rd ed.). San Francisco, CA: Jossey-Bass. Schweiger, D. M., & Sandberg, W. R. (1991). A team approach to top management’s strategic decisions. In H. E. Glass (Ed.), Handbook of Business Strategy, 6 (pp. 1–20). New York, NY: Warren, Gorham and Lamont. Substance Abuse and Mental Health Services Administration (SAMHSA), Office of Applied Studies. (2003). National Survey of Substance Abuse Treatment Services (N-SSATS): 2002. Data on Substance Abuse Treatment Facilities, DASIS Series: S-19, DHHS Publication No. (SMA) 03–3777. Rockville, MD: SAMHSA. Tannenbaum, A. S., Kaycic, B., Rosner, M., Vianello, M., & Weiser, G. (1974). Hierarchy in organizations. San Francisco, CA: Jossey-Bass Publishers. Taxman, F.S., Cropsey, K.L., Melnick, G., Perdoni, M.L. (in press). COD services in community correctional settings: An examination of organizational factors that affect service delivery. Behavioral Science and the Law. Tschan, G. (1995). Communication enhances small group performance if it conforms to task requirements: The concept of ideal communication cycles. Basic and Applied Social Psychology, 17, 371−393. Tulchinsky, T. H., & Varavikova, E. A. (2000). The new public health: An introduction for the 21st Century. San Diego, CA: Academic Press. Wanberg, C. R., & Banas, J. T. (2000). Predictors and outcomes of openness to changes in a reorganizing workplace. Journal of Applied Psychology, 85, 132−142. Welsh, W (2000). Building an effective research collaboration between the Center for Public Policy at Temple University and The Pennsylvania Department of Corrections: Final report. Philadelphia: Center for Public Policy at Temple University, US Department of Justice, National Institute of Justice (Grant# 98-CEVX- 0016). Wejnert, B. (2002). Integrating models of diffusion of innovations: A conceptual framework. Annual Review of Sociology, 28, 297−326. Zander, A. (1994). Making groups effective, (2nd ed.). San Francisco, CA: Jossey-Bass.
Regular article
Constructive conflict and staff consensus in substance abuse treatment Gerald Melnick, (Ph.D.)⁎, Harry K. Wexler, (Ph.D.), Michael Chaple, (M.A.), Charles M. Cleland, (Ph.D.) Center for the Integration of Research and Practice (CIRP), National Development and Research Institutes, Inc., (NDRI), New York, NY 10010, USA Received 20 December 2007; received in revised form 9 April 2008; accepted 5 May 2008
Abstract Previous studies demonstrated the relationship between consensus among both staff and clients with client engagement in treatment and between client consensus and 1-year treatment outcomes. The present article explores the correlates of staff consensus, defined as the level of agreement among staff as to the importance of treatment activities in their program, using a national sample of 80 residential substance abuse treatment programs. Constructive conflict resolution had the largest effect on consensus. Low client-to-staff ratios, staff education, and staff experience in substance abuse treatment were also significantly related to consensus. Frequency of training, an expected correlate of consensus, was negatively associated with consensus, whereas frequency of supervision was not a significant correlate. The implications of the findings for future research and program improvement are discussed. © 2009 Published by Elsevier Inc. Keywords: Constructive conflict; Staff consensus; Organizational culture
1. Introduction 1.1. Prior findings Melnick, Wexler, Chaple, and Banks (2006) have examined the effects of a cohesive organizational culture as defined by consensus on the importance of various therapeutic community (TC), cognitive behavioral, and 12-step activities within substance abuse treatment programs. Two types of agreement were explored, consensus, representing agreement within groups (e.g., primary substance abuse treatment counselors or clients), and concordance, representing agreement between groups (e.g., counselors and clients). In all cases, high levels of agreement, both within and between groups, were associated with higher levels of client engagement in treatment (Melnick et al., 2006). In an additional test of the effect of consensus on treatment ⁎ Corresponding author. Center for the Integration of Research and Practice (CIRP), National Development and Research Institutes, Inc. (NDRI, 71 W 23 Street, 8th Floor, New York, NY 10010, USA. Tel.: +1 212 845 4400; fax: +1 212 845 4650. E-mail address: [email protected] (G. Melnick). 0740-5472/08/$ – see front matter © 2009 Published by Elsevier Inc. doi:10.1016/j.jsat.2008.05.002
outcomes, an analysis of the Drug Abuse Treatment Outcomes Study showed that high levels of client consensus in beliefs about the role of abstinence in recovery at 1 month in treatment predicted drug and alcohol use at 1 year posttreatment follow-up (Melnick, Wexler, & Cleland, 2008). 1.2. Potential implications for treatment programs Organizational culture has been defined as the shared beliefs of an organization's members (Ostroff, Kinicki, & Tamkins, 2003; Schein, 1996, 2004) that determine the attitudes, perceptions, goals, and ultimately, the behavior of its members (Schein, 1996). High levels of agreement are believed to reduce the amount of uncertainty within an organization (Galbraith, 1977) and to engender consistency and predictability in its members' reactions to events, resulting in reduced friction and improved member relationships (Zander, 1994). In a less-cohesive culture, values are vague or ambiguous, which leads to differing interpretations, inconsistent actions, and conflict between members or groups of members (Martin, 2002; Martin & Meyerson, 1988). Thus, in a cohesive culture, core values are consistent throughout the organization and help the
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organization to integrate its various activities and adapt to change (Schein, 2004). A cohesive culture is especially important in health-care-related organizations that depend on the contribution of many different individuals (Tulchinsky & Varavikova, 2000) and in substance abuse treatment programs where trust and cooperation are important to the introduction of new treatment technologies (Lehman, Greener, & Simpson, 2002). Melnick et al. (2006, 2008) have defined consensus as a special case of organizational culture referring to the level of agreement concerning beliefs related to treatment goals and protocols. In substance abuse treatment programs, consensus may involve the goals of treatment, such as total abstinence being the way to recovery, and important aspects of the treatment protocol, such as the use of work as therapy, the use of thought-stopping techniques, or the belief in the importance of recognizing a higher power. When the level of consensus is high, all staff members will teach the same goals and will react in a similar manner, transmitting a consistent message and a clear set of expectations that serve to facilitate the therapeutic process, culminating in higher levels of client engagement and better treatment outcomes. 1.3. Need to understand the antecedents of consensus The study reported here seeks to fill a gap in our understanding of the correlates of consensus in substance abuse treatment programs. Previous studies provided empirical support for the relationship between organizational cohesion, as represented by consensus and concordance, and the performance of substance abuse treatment programs and referred to a well-developed theory within the area of organizational development (Melnick et al., 2006, 2008). These studies, however, have yet to identify the variables responsible for the development of consensus that would permit the experimental manipulation of consensus and inform treatment programs of the relevant organizational interventions. The focus of this study is on staff because the implicit model of the internal effects within programs suggests that the transmission of core values to the clients is heavily influenced by the more permanent members of the community (the primary substance abuse treatment counselors), who will transmit these beliefs to the new members (clients) so that consensus among counselors will lead to more consensus among clients and concordance between counselors and clients. This model follows the conception of culture as a pattern of shared basic ideas that have been successful enough to be considered valid and that are taught to new members as the proper way to approach and solve problems (Schein, 2004). Possible antecedents of consensus have not been collectively compiled, but prior theory and research point to communication as a critical factor. For example, downward communication predominates in many organizations. Under these conditions, supervisors frequently overestimate
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the amount of information that subordinates possess (Likert, 1961), as well as how often and how clearly they have communicated information to subordinates (Callan, 1993). Decentralized communication, consisting of vertical and horizontal patterns of interchange (Clampitt & Downs, 1993; Tschan, 1995), maximizes the sharing of information, is self-correcting for the meaning of the communication, and would be expected to affect the amount of information that counselors share about the treatment process. Program policies regarding open planning and decision-making processes (D'Aunno, FozMurphy, & Lin, 1999; Roman & Johnson, 2002) may also be expected to influence staff consensus. For example, people are more likely to invest in and to commit to policies that they have helped to shape than to policies that were imposed upon them (Cotton, 1995; Sagie, 1995; Wanberg & Banas, 2000). Even when not directly involved in the decision making itself, people are more willing to accept administrator-derived policies if their input has been considered in the actual implementation (Sagie, Elizur, & Koslowsky, 1990, 1995). Conflict resolution is another factor that might be important to building staff consensus. Participation will almost always generate some degree of conflict as staff members with different attitudes or perspectives express their opinions. Organizations with norms for expressing and dealing with conflict will be able to resolve differences and achieve higher levels of cohesion than organizations that either suppress differences or are unable to reach a constructive resolution (Jehn, 1997; Kirchmeyer & Cohen, 1992; Parker, 1993). Therefore, tolerance of disagreement and conflict resolution skills are expected to foster consensus. Several static variables might be expected to affect organizational cohesion. For example, size is related to organizational structure, so that smaller programs are less bureaucratic than larger ones, which may enhance and increase opportunities for the entire staff to interact with one another (Alexander et al., 1997; Moos, 1997). Particularly germane to the present study, program size has been related to variations in program practices (Jonkman, McCarty, Harwood, Normand, & Caspi, 2005). Staff tenure (Knudsen, Johnson, & Roman, 2003; Moos, 1997) affects the opportunity for communication and consensus building over time. Low staff–client ratios (Prendergast, Podus, & Chang, 2000; Welsh, 2000) are associated with more opportunities for staff interaction and with more staff contact per client (Welsh, 2000). In addition, staff training, supervision, experience, and education all contribute to a shared understanding of the program's goals and treatment approach (D'Aunno & Vaughn, 1995; De Leon, 2000; Knudson, Ducharme, & Roman, 2006); consequently, higher levels of each of these are expected to raise levels of consensus among the staff. The study reported here attempts to extend previous findings by identifying factors associated with a cohesive organizational culture characterized by high levels of
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consensus. Because consensus has been shown to influence both treatment process and outcomes, identifying the correlates of consensus will facilitate the formulation of a causal model that will inform the development of interventions to enhance treatment quality.
2. Materials and Methods 2.1. Sampling Sampling and data collection were conducted by the National Opinion Research Corporation of the University of Chicago. Data collection took place between April and September 2004. The sampling frame consisted of the National Directory of Drug and Alcohol Abuse Treatment Programs (Substance Abuse and Mental Health Services Administration [SAMHSA], 2003), which is a listing of federal, state, and local government as well as private facilities that provide substance abuse treatment services. These facilities (a) were licensed, certified, or otherwise approved for inclusion in the directory by their state substance abuse agencies and (b) had responded to the 2002 National Survey of Substance Abuse Treatment Services, which SAMHSA had been conducting annually. The sample was restricted to programs that (a) had been in operation (seeing clients) for a minimum of 3 years; (b) served an adult population; (c) had a minimum planned duration of stay of at least 60 days; and (d) were freestanding (not part of a hospital, school, or university). A random sampling procedure with replacement was used to establish a sample of 80 residential treatment programs. The sample was stratified by program size, with 20 programs included from each of four bed-size categories (i.e., 15–25, 26–50, 51–100, and N100 beds) to prevent bias toward any particular program capacity. To ensure a geographically diverse sample, we selected programs from metropolitan statistical areas (defined by the U.S. Census Bureau) in New York, New Jersey, Massachusetts, New Hampshire, Illinois, Indiana, Wisconsin, Texas, California, and Arizona. A total of 118 programs were determined to be eligible and approached to participate in the study; 38 of these (32%) refused participation. Each program that volunteered to participate in the study was given $1,000 as compensation for completing the survey. Data were collected for the study using the Multimodality Quality Assurance (MQA) instrument (Melnick & Pearson, 2000), which had been developed, tested, and used previously for group administration (Melnick, Hawke, & Wexler, 2004; Melnick & Wexler, 2004). Survey respondents in each program consisted of primary drug treatment counselors who were on duty at the time data were collected; when data were collected, 86.4% of the primary drug counselors were available (the remaining 13.6% were off-site). Surveys were completed in a group setting and facilitated by a trained interviewer who first
explained the process of informed consent and the purpose of the project and who remained available to answer any questions that counselors had in the course of completing the instruments. Data collection sessions were scheduled during staff meetings so that completing the instrument was not perceived as an additional encumbrance and did not interfere with therapeutic activities. Generally, the staff needed 20 to 30 minutes to complete the instrument. Of the 595 staff across the 80 programs, 7 (1%) either refused or were otherwise unable to complete the survey. The lowest response rate of the staff on duty at the time of testing at any program was 78%; 2 other programs included a single staff member who refused to participate. Overall, an average of 86% of program staff participated in study. Table 1 shows the characteristics of the programs included in the study. Most programs (65%) were described by the administrator as TCs. Programs were well established, admitting clients for a median of 22 years. The median enrollment (i.e., measured over the most recent 12-month period) was 70 clients, and the median ratio of primary drug counselors to clients was 1:12. Table 2 shows the characteristics of primary drug treatment staff employed at these programs. Most of the staff (68%) had at least some college education, most (59%) had substance abuse treatment training outside the program, and just under half (49%) had substance abuse treatment credentials. More than three quarters (77%) of the staff had 3 or more years of experience in treating substance abuse, and most (61%) had been employed at their present program for more than 2 years.
Table 1 Program characteristics Characteristics Program orientation (%) TC Cognitive–Behavioral therapy Mutual self-help/12-step Other Length of time (in years) admitting clients (median) Average enrollment in the past 12 months (median) Ratio of clients to drug counselors (median) Ratio of clients to all counselors (median) Frequency of group staff meetings (mean) Frequency of individual staff meetings (mean) Client demographics (%) Proportion of male clients (median) Proportion of Hispanic clients (median) Proportion of Black clients (median) Proportion of White clients (median) Staff demographics (%) Proportion of male staff (median) Proportion of Hispanic staff (median) Proportion of Black staff (median) Proportion of White staff (median)
Residential programs sampled (n = 80) 65 7 20 8 22 70 12.07 3.82 2.74 (0.83) 3.31 (1.2) 77 21 30 47 50 11 33 52
G. Melnick et al. / Journal of Substance Abuse Treatment 36 (2009) 174–182 Table 2 Staff characteristics Characteristics Staff education (%) Lower than high school High school or GED Some college Associate's degree Bachelor's degree Some graduate school Staff credentials (%) Substance abuse training outside the program Substance abuse credentials Mental health credentials Years working in substance abuse treatment field (%) b1 year 1–2 years 3–5 years N5 years Staff tenure: length of employment in program (%) b6 months 6 months–1 year 1–2 years N2 years Staff receiving in-service training (%)
Residential programs sampled (n = 80) 4 28 28 17 16 7 59 49 9
8 15 27 50 15 9 15 61 31
2.2. Measures 2.2.1. Treatment implementation Measures of treatment implementation consisted of three scales from the MQA instrument (Melnick & Pearson, 2000; Melnick & Wexler, 2004; Melnick et al., 2004) that examine TC, cognitive behavioral treatment (CBT), and 12step treatment elements, three of the most frequently used residential treatment modalities (Lipton, Parson, & Wexler, 1999). Each scale consists of 15 Likert-type items in which statements are rated from 0 (not at all important in our program) to 3 (extremely important in our program). Some examples for items from each modality include the following: “clients confront unacceptable behavior outside individual and group counseling” (TC); “[program] explains the use of thought-stopping techniques” (CBT); and “[program] emphasizes the need to rely on a higher power” (12-step). A previous factor analysis found the scales were unidimensional with good reliability (Melnick et al., 2004). This study followed the recommendation previously reported (Melnick et al., 2004, 2006), combining the TC, CBT, and 12-step treatment scales to achieve a better assessment of the overall treatment experience. This approach was supported by an analysis of variance of staff mean scores for the three scales, which found no difference between mean scores in programs described by their directors as TC, CBT, or 12-step. In addition, the overall coefficient of reliability (Cronbach's alpha) scale reliability for the combined measure was .94 for staff; this high alpha coefficient was equivalent to the alpha
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coefficients for the individual TC, CBT, and 12-step treatment scales, which ranged from .82 to .95, a finding that further supported combining scales into a single measure of treatment implementation. 2.2.2. Organizational cultural norms for communication Program communication was investigated as a reflection of the organizational culture insofar as communication represents the organization's core beliefs and norms regarding the expected interactions of its members. Specifically, program communication is defined by three unique scales that explore multilevel communication, planning, and decision making and conflict resolution. The multilevel communication scale is composed of seven similar questions that examine interpersonal lines of communication between program members to determine the direction of the communication flow across organizational levels within the program. The seven items were constructed using the logic of sociometry (see, for example, Newcomb, Bukowski, & Pattee, 1993), which, for this study, examines interrelationships within the larger program structure. An example of an item on this scale is “Program director starts discussions with counselors regarding problems or concerns about the program.” For each statement, counselors were asked to indicate the frequency of occurrence in their program, with responses ranging from 0 (never) to 3 (often). The alpha reliability for this seven-item scale is .78. The planning and decision-making scale was adapted from the Staff Decision-Making Scale (Parker, 1993; Tannenbaum, Kaycic, Rosner, Vianello, & Weiser, 1974) and is designed to measure staff feedback, specifically the extent to which supervisory staff and program directors solicit opinions and suggestions from counselors when making decisions about program policies and/or treatment issues. An example of an item on this scale is “How often does the program director ask for counselor's opinions and suggestions about treatment issues?” The total scale consists of six items that ask respondents to indicate the frequency of occurrence in their program, with responses ranging from 0 (never) to 3 (often). The alpha reliability for this six-item scale is .86. The nine-item constructive conflict resolution scale was adapted from previous work on organizational conflict (Kirchmeyer & Cohen, 1992). The nine items measure the extent to which staff express and resolve task-oriented conflicts in a productive and constructive manner. An example of an item on this scale is “We have open and frank discussions about our differences.” Counselors were asked to indicate the extent to which each statement is true, that is, “not true,” “somewhat true,” mostly true,” or “very true.” Three negative statements requiring reverse scoring (e.g., “people are afraid to speak up for fear of ridicule or retaliation”) were removed to eliminate the negative impact of the method variance (Doty & Glick, 1998; Malhotra, Kin, & Patil, 2006) on scale reliability. Indeed, the alpha
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reliability of .64 for the nine-item scale improved to .87 when three negatively phrased items were removed. 2.2.3. Formal program activities expected to affect consensus Direct measures expected to influence consensus included the number of training sessions in the last year and the frequency of group and individual supervisory meetings in which clients were discussed (e.g., daily, two or three times a week, weekly, monthly, less frequently than once a month). 2.2.4. Program and staff characteristics that may indirectly affect consensus Indirect measures that might affect consensus included education level (ranging from no high school diploma or GED [General Educational Development] to graduate school education); staff experience (number of years spent working as a substance abuse counselor); and primary treatment counselor-to-client ratios (numbers of primary treatment counselors and current clients, which reflect staff time available to reflect or communicate about treatment activities). 2.3. Analytic strategy 2.3.1. Calculating consensus on treatment implementation The measurement of consensus was derived from the agreement among staff regarding the level of treatment implementation in their respective programs. A mean score was aggregated to the program level, and the measure of consensus was determined primarily from the standard deviation from that mean score. The precise calculation of consensus follows prior work (Melnick et al., 2008) and includes a minor modification to an earlier formula (Melnick et al., 2006). In this calculation, the within-group variance on “treatment implementation” is indicated by a score that tracks positively with consensus and that can range from 0 (no consensus) to 1 (perfect consensus). Because larger standard deviations equate to less agreement, the measure is reversed so that larger scores represent greater agreement. For each program, the maximum possible standard deviation was determined; that is, the standard deviation that would be observed if responses were split evenly between the highest and lowest points on the scale. For example, if 20 staff in a program were to respond, the maximum possible standard deviation would be observed if 10 of those staff were to respond with 0 and the other 10 with 3 (SD = 1.539). The observed standard deviation in the program was divided by this number, with the result subtracted from 1. Thus, the formula for calculating consensus can be expressed as: 1
observed SD maximum possible SD
The correlation between the derived ratio and the observed standard deviation was 0.99. Although the maximum possible SD is program specific, the introduction of the ratio simplifies comparisons between different programs and studies.
2.3.2. Data analysis In recognition of the special role of the mean score in determining consensus, partial correlations, controlling for the consensus mean sore, were used to determine the association between the separate measures and staff consensus as to the importance of the various TC, CBT, and 12-step treatment activities in the substance abuse treatment program. Multiple regression was then used to examine the unique association of each independent variable with consensus. Variables entered in the first of two blocks included (a) the mean score of the treatment implementation scale; (b) the projected indirect program and staff influences on consensus; and (c) the projected direct program influences on consensus. The three variables that characterized organizational cultural norms regarding communication within the program were entered in the second block. These variables were entered in a separate block because they were conceptually related to each other and qualitatively different from the other independent variables that captured more structural and less dynamic aspects of programs. Grouping the organizational cultural norms regarding communication together also allowed for examination of the association of those variables as a group with consensus. The mean score signifying the level at which treatment activities were considered important was included as a control variable because consensus is reduced when the mean approaches its floor or ceiling. 3. Results As expected, the correlation between the mean score of the treatment implementation scale and consensus on this same scale was relatively large and significant (r = 0.568, p b .001). Table 3 examines the partial correlations between the possible correlates in the study and consensus after controlling for the effects of the treatment implementation mean score. Only the
Table 3 Partial correlations between main study variables and staff consensus controlling for mean treatment implementation scores Staff consensus Staff tenure Program size a Client: drug counselors Staff experience level Staff education level Staff training level Frequency of individual staff meetings Frequency of group staff meetings In service training Multilevel communication Planning and decision making Conflict expression/resolution
.209 .003 .228 .032 .088 −.288 ⁎ −.085 −.038 −.058 .031 .209 .374 ⁎⁎
a Average number of participants enrolled during past 12 months. ⁎ Significant at b.05. ⁎⁎ Significant at b.001.
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frequency of staff training and constructive conflict resolution were significantly correlated with consensus, with training showing a negative correlation. 3.1. Effects of organizational cultural norms regarding communication on staff consensus Table 4 shows the collective impact of key program characteristics on staff consensus regarding the level of treatment implementation. The first block of independent variables accounted for 45% of the variance in consensus (p b .01). Prior to the inclusion of the communication/ organizational culture variables in the second block, only the mean treatment implementation and the staff training level were significant, with the latter showing a negative relationship to consensus. The addition of the variables in the second block accounted for a significant increment in the variance in consensus (ΔR2 = 0.216, p b .001). With all variables in the model, the ratio of drug counselors to clients, staff education, training, and staff experience each demonstrated a positive relationship with staff consensus. Controlling for all other variables, constructive conflict resolution emerged as the strongest predictor of staff consensus.
4. Discussion Conflict resolution had the largest unique association with staff consensus regarding the belief in the importance of various treatment activities. Table 4 introduces the communication scales as a block; additional analyses (not shown) investigated the effect of adding the communication variables singly to the model, but no significant relation with consensus emerged, whereas the planning and decision making scale was significant when added by itself to the model, as was the constructive conflict
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resolution scale. The correlation between these latter two scales was 0.71, indicating a strong association between programs that actively engage their staff in planning and decision making and the ability of the program to deal with conflict constructively. Staff education and experience were both associated with consensus, but only in the presence of organizational norms for communication. This suggests that when a program has a culture with norms for conflict resolution that permit dissent to be voiced and resolved, the experience and education of its staff might be better integrated or otherwise utilized. A similar effect was noted for staff-to-client ratios, where low ratios were associated with high staff consensus only after the block of communication variables was added. This provided an additional instance in which the cultural variables related to communication, and conflict resolution appeared to have facilitated the association between other program or staff characteristics and consensus. Activities expected to have a direct effect on staff forming a common set of beliefs about treatment, such as training and supervision, were either neutral or negative in their relationship with consensus, which remains to be explained. It may be that high levels of training or supervision are the programs' response to low levels of consensus within their staffs. In addition, if some staff members are trained and some are not, in the absence of discussion and conflict resolution, different perspectives could emerge and undermine consensus. The question remains as to how organizational cultural factors related to communication affect consensus. Conflict is generally considered to be disadvantageous to an organization in achieving its goals but can serve to the benefit of organizations when organizational norms for expressing and resolving conflict are present (Aritzeta, Ayestaran, & Swailes, 2005; Desivilya & Yagil, 2005). Under this latter condition, conflict can lead to innovation at
Table 4 Staff ratings of communication scales predicting consensus (controlling for informal and formal measures) Model I b Block I—program and staff characteristics Staff treatment mean score .195 Staff tenure −.005 −.000 Program size a Client: drug counselor .001 Staff experience level .110 Staff education level .137 Staff training level −.167 Frequency of individual meetings −.018 Frequency of group meetings .016 In service training −.001 Block II—organizational norms for communication Communication Planning and decision Constructive conflict resolution a
Model II B
t/f
p
b
B
t/f
p
.433 −.013 −.123 .236 .196 .211 −.274 −.152 .069 −.035
2.87 −.090 −.669 1.40 1.40 1.54 −2.33 −1.19 .758 −.747
.007 .929 .508 .171 .169 .133 .026 .243 .453 .460
.101 −.042 .000 .003 .163 .176 −.162 −.009 .011 .000
.224 −.113 −.293 .505 .290 .271 −.320 −.089 .076 −.041
1.65 −.924 −1.81 2.72 2.12 2.40 −2.72 −.699 .602 −.371
.109 .363 .080 .010 .042 .022 .010 .489 .552 .713
−.015 −.044 .191
−.043 −.182 .685
−.349 −.848 3.26
.729 .403 .003
Measured by average number of participants enrolled during past 12 months.
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the organizational level and staff growth (Cloke, Goldsmith, & Bennis, 2005; Runde & Flanagan, 2007). To some extent, the effects of conflict depend on its perception or on the beliefs regarding the nature of conflict within the culture of the organization. Conflicts within organizations can be perceived as either affective, focusing on the individuals or groups who attain power, or substantive, focusing on goals (e.g., abstinence or moderate use of drugs in recovery) or the means of achieving goals (e.g., the use of work as a therapeutic activity). Regardless of the type of conflict, people tend to interpret dissent affectively as personal attacks rather than as substantive differences (Ginzel, 1994; Jehn, 1997). Constructive communication norms can neutralize this tendency by influencing the interpretation of disagreements within the organization to favor substantive rather than affective conclusions (Bottger & Yetton, 1988; Jehn, 1995; Schweiger & Sandberg, 1991). Where differences are perceived as substantive, organization members feel free to express disagreement and believe their opinions are respected (Jehn, 1995). Moderate levels of conflict can be constructive, stimulating wider discussion of ideas and resolving differences between members, ultimately building consensus. In this manner, organizations that permit dissent and have constructive methods for dealing with disagreements will be able to reach more lasting consensus than those organizations that either suppress differences to avoid conflict or are otherwise unable to achieve resolution (Graham & Keeley, 1992; Parker, 1993). In the context of this study, norms that favor conflict resolution might encourage more experienced or educated staff to contribute to the program and could provide a mechanism for identifying and reducing differences. Conflict resolution would appear to play an important role in the delivery of treatment and the adoption of new technology, as consensus would be expected to increase the consistency and longevity with which a treatment protocol is delivered, teaching new staff as part of the informal transmission of culture within the organization (Schein, 2004). In this vein, it is interesting to note the possible connection between constructive conflict and the role of open communication (Coiera, 2003; Kaplan, 1997; Klein, Conn, & Sorra, 2001; Taxman, Cropsey, Melnick, & Perdoni, in press; Wejnert, 2002) and consensus building (Bero et al., 1998) in the adoption, implementation, and fidelity of best practices. The perspective is a dynamic one in which organizations that continuously consider and resolve differences are more likely to reach agreement on ever improving policies, procedures, and processes (Runde & Flanagan, 2007). Such organizations may also be more likely to engage in continuous quality improvement because the effectiveness of clinical interventions would be more likely to be discussed. It is also important that this study takes place in a context in which staff and client consensus about the treatment being implemented has been shown to be associated with client engagement in treatment (Melnick et al., 2006), and
client consensus concerning abstinence as the goal of recovery at 1 month in treatment has been shown to influence 1-year treatment outcomes (Melnick et al., 2008). Thus, we suspect conflict resolution increases staff consensus about the treatment protocol, which in turn increases client engagement in treatment, and that these increased levels of client engagement result in better treatment outcomes. This model has yet to be tested in longitudinal study beginning with the experimental manipulation of consensus by means of an organizational intervention to improve norms for conflict resolution. Although conflict resolution is a strong correlate of staff consensus in the study and offers an attractive theoretical basis for understanding the development of staff consensus, several limiting factors need to be considered. As noted above, although the study reports a strong unique association between conflict resolution and staff consensus with some confidence, evidence for a causal connection awaits the experimental manipulation of organizational norms concerning conflict resolution along with the longitudinal study of the influence of consensus on client engagement and outcomes. 4.1. Limitations This study had some limitations that may affect the generalization of the results. The sample was restricted to long-term residential treatment programs, where staff might interact differently than in outpatient programs; therefore, current findings concerning conflict resolution might be limited to residential programs. Second, although the study represented a geographically diverse group of programs, it was limited to the 68% of the programs contacted that agreed to participate and to the 86% of staff available at the time data were collected (the staff refusal rate was small at only 1%). In addition, the stratified sampling procedure resulted in oversampling the larger programs, so that the effect of these programs is overrepresented in the findings. Furthermore, of the myriad factors that have the potential to affect staff consensus, only a limited number have been considered in this study; other variables might prove to be more closely associated with staff consensus.
5. Summary In summary, conflict resolution was found to be an important correlate of staff consensus regarding the value of various treatment activities and appeared to facilitate the effects of other variables, such as counselor-to-client ratios and staff education and experience, in creating higher levels of staff consensus. These results are promising in pointing the way to the development of interventions that will increase staff consensus and to the formulation of causal relationships among conflict resolution, staff consensus, and program outcomes.
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