Counselor skill influences outcomes of brief motivational interventions

Journal of Substance Abuse Treatment 37 (2009) 151 – 159

Regular article

Counselor skill influences outcomes of brief motivational interventions Jacques Gaume, (M.A.)⁎, Gerhard Gmel, (Ph.D.), Mohamed Faouzi, (Ph.D.), Jean-Bernard Daeppen, (M.D.) Alcohol Treatment Centre, Lausanne University Hospital, 1011 Lausanne, Switzerland Received 15 August 2008; received in revised form 4 December 2008; accepted 18 December 2008

Abstract The aim of this study was to estimate the influence of counselor skills during brief motivational interventions (BMIs) on patient alcohol use 12 months later. Ninety-five BMIs delivered by five counselors of similar background and training were recorded and coded using the Motivational Interviewing Skills Code (MISC). Baseline alcohol measures and sociodemographics of patients did not differ across counselors, whereas MISC scores and outcome at 12 months did. Multilevel models showed that counselors with better motivational interviewing (MI) skills achieved better outcomes overall and maintained efficacy across all levels of an important predictor (patient ability to change), whereas counselors with poorer MI skills were effective mostly at high levels of ability to change. Findings indicated that avoidance of MI-inconsistent skills was more important than frequency of using MI-consistent skills and that training and selection of counselors should be based more on the overall MI-consistent gestalt than on particular MI techniques. © 2009 Elsevier Inc. All rights reserved. Keywords: Brief motivational intervention; Counselor influence; Skills; Motivational interviewing skill code; Alcohol

1. Introduction Brief motivational intervention (BMI) is an adaptation of motivational interviewing (MI, Miller & Rollnick, 2002) with single, short sessions of 15 to 60 minutes each. BMI focusing on alcohol has been associated with decreases of approximately 20% in consumption and is often as effective as more intensive treatments (Bertholet, Daeppen, Wietlisbach, Fleming, & Burnand, 2005; Bien, Miller, & Tonigan, 1993; D'Onofrio & Degutis, 2002; Dunn, Deroo, & Rivara, 2001; Emmen, Schippers, Bleijenberg, & Wollersheim, 2004; Kaner et al., 2007). Reviews on strategies targeting alcohol consumption showed that BMI, along with structural measures such as driving while intoxicated regulations and control of prices and taxes, was the most cost-effective strategy among individual-centered approaches and one of the few effective preventive strategies (Babor et al., 2003). The evidence for the efficacy of alcohol BMI is strong,

⁎ Corresponding author. Alcohol Treatment Centre, Mont Paisible 16, 1011 Lausanne, Switzerland. Tel.: +41 21 314 01 39; fax: +41 21 314 05 62. E-mail address: [email protected] (J. Gaume). 0740-5472/08/$ – see front matter © 2009 Elsevier Inc. All rights reserved. doi:10.1016/j.jsat.2008.12.001

suggesting a modest decrease in alcohol consumption in nondependent hazardous drinking patients, particularly in primary care settings (Bertholet et al., 2005). It remains controversial whether BMI is effective when there is less interaction between patients and providers than in primary care settings (Saitz, Svikis, D'Onofrio, Kraemer, & Perl, 2006). For example, the data herein come from a randomized controlled clinical trial evaluating the effectiveness of BMI in reducing hazardous alcohol use in an emergency department (ED) and show that a 15-minute BMI session did not reduce hazardous alcohol use at 12 months follow-up (Daeppen, Gaume, et al., 2007). This null finding emerged, although the sample included patients previously considered likely to benefit from BMI, such as non-alcoholdependent hazardous drinkers and young patients attending the ED after an injury. These results are consonant with uneven findings regarding the efficacy of alcohol BMI in ED, suggesting that the intervention itself, the counselors, the patients, or the patient–counselor interaction may influence the efficacy of BMI. These null findings call for deeper analysis and for the exploration of BMI contents and interactions between counselor and patient. Indeed, little is known about exactly

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what works in BMI and which of the counselor and patient behaviors are most effective in triggering behavior changes. The first contribution to the literature regarding BMI mechanisms has been the identification of six common factors used in effective brief interventions trials, summarized under the acronym FRAMES (Bien et al., 1993; Miller & Sanchez, 1993): Feedback regarding personal risk or impairment, emphasis on personal Responsibility for change, clear Advice to change, a Menu of alternative change options, therapeutic Empathy as a counseling style, and enhancement of client Self-efficacy or optimism. The authors demonstrated that these six factors were present in effective brief interventions but did not evaluate their role in stimulating patient change. To better understand the BMI mechanisms in the aforementioned ED trial (Daeppen, Gaume, et al., 2007), we recently conducted two studies (Daeppen, Bertholet, Gmel, & Gaume, 2007; Gaume, Gmel, & Daeppen, 2008). Both showed that patient characteristics (perceived ability to change and the objective setting at the end of BMI) were associated with alcohol use at follow-up in the expected direction but were independent of counselor MI skills. These findings led to the question of the counselor's role in influencing patient outcome. In a study on the impact of several counselors in substance abuse rehabilitation outcomes (McLellan, Woody, Luborsky, & Goehl, 1988), one counselor significantly influenced outcomes in a positive way, whereas another did so in a negative way. Background and formal education among the counselors were not related to observed performance differences, but variations in the counseling content and process among the counselors were associated with different patient outcomes. In a report of therapist effects in four large outcome studies, Luborsky et al. (1986) observed considerable variability in outcome within the caseloads of individual therapists and concluded that therapist effect sizes generally overshadowed any differences between different forms of treatment. In a review on therapist effectiveness in treating substance use disorders (Najavits & Weiss, 1994), the authors concluded that therapists show diverse rates of effectiveness; these differences appear to be independent of either the professional background of therapists or patient factors at the start of therapy. Strong interpersonal skills of therapists were the main characteristic associated with greater effectiveness. More recently, Luborsky, McLellan, Diguer, Woody, and Seligman (1997) observed that there were important differences in the improvement levels and posttreatment outcomes in the caseloads among 22 therapists; these differences could not be explained by differences in patient background or severity but appeared to reflect therapist interactions with patients. Several MI and BMI studies did address counselor impact, but most focused on the effectiveness of certain skills or counseling styles. Therapist skills influenced patient involvement, engagement, reactance, speech, and the therapeutic alliance within MI sessions (Boardman, Catley, Grobe, Little, & Ahluwalia, 2006; Catley et al., 2006; Karno

& Longabaugh, 2005; Moyers, Miller, & Hendrickson, 2005). In a study comparing a directive–confrontational to MI-based client-centered counseling, Miller, Benefield, and Tonigan (1993) found that the directive–confrontational style induced significantly more resistance from clients, which in turn predicted poorer outcomes 1 year later. Therapist styles generally did not differ in overall impact on drinking, but a single therapist behavior was predictive of outcome, that is, the more the therapist used confrontation, the more the client drank. Recent research showed that MIconsistent therapist behaviors were more often followed by patient self-motivational statements, whereas MI-inconsistent behaviors were more likely to elicit patient resistance. Client speech during sessions appeared to be a powerful predictor of substance abuse outcome, thus providing preliminary support for a causal chain between therapist behaviors, subsequent client speech, and drinking outcomes (Moyers et al., 2007). We found only one MI study addressing the influence of individual counselors rather than evaluating single skills that may sum up differently across different counselors (Project MATCH Research Group, 1998). The authors examined therapist differences within a trial of three psychosocial treatments for alcohol problems (12-step facilitation, cognitive–behavioral skills training, and motivational enhancement therapy). They found significant therapist effects in client satisfaction and outcomes, even after controlling for treatment sites and client baseline characteristics. Although important outcome differences were found among therapists, these authors could not account for them from individual attributes of therapists (e.g., personality characteristics). They concluded that outcome variance might be more closely tied to therapist behaviors during treatment. The purposes of this article are to (a) test the hypothesis that MI skills during BMI differ across counselors despite similar backgrounds and training, (b) demonstrate that skill differences influence alcohol use outcomes in patients after BMI, and (c) because it was already shown that patient ability to change has an impact on alcohol use at follow-up (Gaume et al., 2008), to analyze whether counselors were differentially effective across various levels of patient ability to change.

2. Materials and methods 2.1. Study design This study used data from a randomized controlled trial conducted in the Emergency Department of the Lausanne University Hospital, Lausanne, Switzerland, aimed at evaluating the effectiveness of BMI in decreasing hazardous alcohol consumption at 1-year follow-up (Daeppen, Gaume, et al., 2007). All protocols were approved by the Ethics Committee of Lausanne University Hospital. Of the 8,833 patients attending the ED for general reasons who consented

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to screening, 1,366 were positive for hazardous alcohol consumption. This was defined according to National Institute on Alcohol Abuse and Alcoholism (NIAAA) standards: 14 drinks or more per week or more than 4 drinks on a single occasion in the past 30 days (for men aged less than 65 years), and 7 drinks per week or more than 3 drinks on a single occasion (for men aged 65 or older or women of all ages). One drink was defined as a regular glass of wine, a regular beer, or a single shot of spirits (straight or mixed in a soft drink) containing about 10 to 12 g of pure alcohol. After signing informed consent, 486 patients were randomized to a BMI group and 880 patients to two control groups. Of these subjects, 1,055 (367 in the BMI group and 688 in the control groups) were successfully followed-up at 12 months. The experimental group received a single BMI session (Zweben, Rose, Stout, & Zywiak, 2005) lasting approximately 15 minutes, which included six steps: (a) thank the patient for participation, reassure the patient about confidentiality; (b) provide feedback about individual alcohol use, compared to similar measures for men and women in the Swiss community and ask the patient for their opinion of the feedback; (c) ask the patient to explore the pros and cons of their alcohol use; (d) on 10-point scales, explore the importance and readiness to change; (e) ask if the patient feels ready to set an objective and provide positive reinforcement about individual ability to achieve this objective; and (f) give each patient written material about their Alcohol Use Disorders Identification Test score (Saunders, Aasland, Babor, Delafuente, & Grant, 1993), drinking pattern percentiles compared to the local community, and drinking objectives. Counselors were six master-level psychologists with 1 year of clinical practice and an experienced nurse. A senior physician and a psychologist experienced in teaching MI and BMI trained the counselors, first with a 2-day workshop on MI containing exercises aimed at improving performance using an active, empathic listening style that minimizes confrontation (Baer et al., 2004) and second with a 5-day workshop focused on trial information procedures as well as on practice of the six-step standardized BMI. Supervision was ongoing during the project, either in the presence of patients or through taperecordings of the BMI. All counselors received uniform amounts of training and supervision. 2.2. Tape-recording and coding Between June 2003 and June 2004, 166 consecutive BMI sessions were tape-recorded with patient consent. Excluded were 33 tape-recordings of subjects lost to follow-up, 25 with incomplete records, 7 with mismatched identification codes, 3 who were not sufficiently fluent in French, and 1 whose wife intruded during the session. Two counselors tape-recorded only 1 session; these 2 recordings were also excluded because (a) their scores would not have been representative and (b) there would not have been variance at

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the second level of our multilevel models (see below). In all, 95 tape-recorded BMI sessions were used in the present analysis provided by five counselors having exactly the same clinical experience and identical training in MI and BMI. Two masters-level psychologists independently carried out coding of the 95 tape-recordings, blinded to assessment and follow-up data; both were trained in MI and in using the Motivational Interviewing Skills Code (MISC), Version 2.0 (Miller, Moyers, Ernst, & Amrhein, 2003). MISC training consisted of simultaneous then independent coding of BMI sessions with discrepancies resolved by an expert. This process lasted until a sufficient interrater reliability for each code was reached during training. The same process was ongoing weekly during the coding of the presented data. The MISC is a behavioral coding system that evaluates therapist and client functioning during MI treatment sessions. The first version of MISC generally offered good interrater reliability in capturing global dimensions and acceptable reliability for specific behavior counts, supporting its use to investigate both the integrity and process of MI (Moyers, Martin, Catley, Harris, & Ahluwalia, 2003; Moyers, Miller, et al., 2005). MISC Version 2.0 was developed to improve in reliability, efficiency, and relevance to training and clinical practice (Miller et al., 2003). Other coding systems like the Motivational Interviewing Treatment Integrity (Moyers, Martin, Manuel, Hendrickson, & Miller, 2005), the Behavior Change Counseling Index (Lane et al., 2005), the Motivational Interviewing Supervision and Training Scale (Madson, Campbell, Barrett, Brondino, & Melchert, 2005), or the Rating Scales for the Assessment of Empathic Communication in Medical Interviews (Nicolai, Demmel, & Hagen, 2007) are available and offer better psychometric properties. However, all of these evaluate only counselor aspects and were designed to assess treatment integrity and not for research on the therapeutical process. Details for the coding process of the present data and its psychometric properties are described elsewhere (Gaume et al., 2008). Briefly, there was globally good interrater reliability even if some codes were not assigned equally by the two coders. Besides, we used summary scores in this study (see below). Summary scores commonly have higher reliabilities compared to the single indicators (Moyers et al., 2003). The MISC 2.0 is composed of global ratings and behavior counts. Two passes are made through each tape-recorded session. The first, uninterrupted pass assesses global ratings. The coder listens to the whole session and then assigns a number on a 7-point Likert scale from 1 (low) to 7 (high) on each of four dimensions: counselor level of Acceptance, Empathy, and MI Spirit and patient degree of Selfexploration. Counselor global ratings are intended to capture the rater's overall impression of counselor performance during the interview. On the second pass through the taperecordings, the coders assign specific behavior counts by listening to the session and categorizing each utterance into 1 of 19 counselor and eight patient codes. Counselor behaviors

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are the following: Advise with permission, Advise without permission, Affirm, Confront, Direct, Emphasize control, Facilitate, Filler (i.e., salutations, pleasantries, and so forth), Giving information, Closed question, Open question, Raise concern with permission, Raise concern without permission, Simple reflections, Complex reflections, Reframe, Structure, Support, and Warn. Patient behaviors are six kinds of “Change talk” (inclination toward or away from the target behavior change), Following or neutral utterances (no inclination or link with the target behavior), and Patient questions. Each patient change talk utterance is also assigned a strength value ranging from +5 (strong inclination toward change) to –5 (strong inclination away from change). Several summary scores are proposed to serve as outcome measures for determining competence in MI (Miller, 2000): the frequency of MI-consistent behaviors (Advise with permission, Affirm, Emphasize control, Open question, Simple and Complex reflections, Reframe, and Support), the frequency of MI-inconsistent behaviors (Advise without permission, Confront, Direct, Raise concern without permission, and Warn), the percent of MI-consistent behaviors (MI-consistent / [MI-consistent + MI-inconsistent]), the percent of complex reflections (Complex reflections / [Complex reflections + Simple reflections]), the percent open questions (Open questions / [Open questions + Closed questions]), and the reflection to question ratio (total reflections / total questions). In the analyses reported further, we used the six summary scores and the three global ratings to characterize counselor MI skills. One kind of patient change talk, Ability to change, was used in the models because it has been described as an important predictor of change in previous analyses (Gaume et al., 2008). Ability to change is calculated as the average strength on the –5 to +5 scale for the whole intervention. 2.3. Descriptive measures and tests We tested differences among counselors on sociodemographic variables, alcohol outcome, and expressed ability to change of their patients, as well as their MI skills. Statistical tests were performed using analyses of variance (ANOVA) for continuous variables and Pearson's chi-square for categorical variables. Potential differences in patient sociodemographic data were tested among counselors because patients were not randomly assigned to counselors. Alcohol outcome was calculated as the baseline to 12 months followup difference in weekly drinking amount (number of standard drinks per week). Differences between counselor MI skills as well as strength of ability to change expressed by their patients were calculated using MISC scores as described above. Tukey's test of pairwise comparisons evaluated whether the nonrandom allocation of patients lead to significant overall baseline differences in patient characteristics across all counselors. According to Hsu (1996), Tukey's test is one of the best approaches to control for Type I errors in multiple pairwise comparisons.

2.4. Multilevel models The change in mean alcohol consumption between baseline and 12 months follow-up was used as a dependent variable. In previous analyses of these data, patientperceived ability to change during BMI was the best predictor of change at the patient level (Gaume et al., 2008); thus, it was used in all models as the first-level (patient) equation. Multilevel models (also called mixed models) using the “xtmixed” procedure of STATA, Version 10.0 (StataCorp, 2007) estimated the effect of counselor skills on the link between patient-perceived ability to change during BMI and behavior change (i.e., mean change in alcohol use). Multilevel modeling also adjusts for design effects due to clustering of patients within counselor (Hox, 2002). We introduced counselor MI skills derived from the MISC global ratings and summary scores in the counselor level of the models. MISC scores for each counselor were aggregated as means of their skills across the interventions of the respective counselor (counselor level, second-level variables). We estimated full cross-level interaction models (i.e., the impact of average counselor skills on both the random intercept and the random slope). The stated formal full cross-level model follows: Change in alcohol useij = b0j + b1j ðability to changeÞ + rij with b0j = g00 + g01 ðmeans of counselor skillsÞ + u0j and b1j = g10 + g11 ðmeans of counselor skillsÞ + u1j ;

where j indicates the counselors and i the patients nested within a counselor, rij is the error at the individual letter, and u0j and u1j are the errors of the intercepts and the slopes, respectively. We first analyzed the model that links patient-perceived ability to change during BMI and behavior change in terms of weekly drinking amounts without taking into account counselor skills (without γ01 and γ11) to show differences in efficacy at different levels of patient ability to change across different counselors, according to clustering of patients within counselor. Results for fitted regression lines are graphically presented using the “two-way” option in STATA. In an attempt to explain differences in regression lines by means of counselor skills, nine separate (six summary scores and three global ratings) models were estimated (full crosslevel model). The interpretation of these effects is as follows: β0j represents the mean consumption change in patients of a counselor j at the value of an ability to change of 0 (i.e., the midpoint of this scale). Thus, β0j can be interpreted to mean the general success (efficacy) level of a counselor in changing the alcohol consumption of patients. This level can vary across counselors. γ01 indicates whether this variability is predicted by the mean level of counselor skills. A positive β1j for each counselor indicates a consumption change of patients increasing on average with the patient ability to change (e.g., efficacy of counseling increasing with

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patient ability to change). The increase in counselor effectiveness can be stronger, depending on their skills, expressed by γ11. Two examples are a high β0j and a β1j close to 0 would mean that a counselor was effective in reducing alcohol use, independent of whether the patients perceived themselves able to change at baseline, and a low β0j and a high β1j would mean that counselors are mainly able to achieve behavior change if patients perceive themselves as able to change. (More precisely, efficacy is low at the 0 ability to change score and rises with increasing patient ability to change.)

3. Results Table 1 presents patient sociodemographic and alcohol consumption data clustered by counselor, as well as counselor MISC scores. Comparisons among counselors showed that there were no significant differences in patient sociodemographic and baseline alcohol use. There were significant differences across counselors in strength of ability to change expressed by their patients. However, Tukey's test showed that the overall significance was due to Counselor 2, whose patients on average had significantly higher scores on their ability to change. Weekly drinking amounts at 12 months and at baseline to 12 months were significantly different across counselors, indicating differential counselor impact on outcomes. MI skills according to MISC scores differed widely among counselors. For example, Counselor 4 made the highest (90%) use of MI-consistent skills, and his

Fig. 1. Link between patient ability to change expressed during BMI and alcohol outcome according to clustering of patients within the five counselors. Ability to change values represent the mean of the strength of ability utterances on a –5 (strong inability to change) to +5 (strong ability to change) scale as measured in the MISC 2.0. Alcohol outcome is represented as the fitted values for the baseline to 12 months follow-up difference in number of drinks per week, negative values indicating a decrease and positive values an increase.

patients had the highest average reduction (−6.9 glasses per week). On the other hand, Counselor 5 had a higher frequency of MI-consistent behaviors (on average 31.1 per BMI) compared to an average of 28.2 for Counselor 4 but had the poorest success in alcohol consumption outcome, with an average increase of 13.4 drinks per week by his patients. We first analyzed the model that linked patient-perceived ability to change during BMI and outcome to show differences in counselor efficacy at different levels of patient

Table 1 Patient sociodemographic and alcohol outcome data and MISC scores clustered by counselors Counselor Variables Sociodemographic data Age, M (SD) % men % Swiss origin % employed Patient ability to change (average strength, −5 to +5 scale), M (SD) Alcohol weekly drinking amount (standard drinks per week) At baseline, M (SD) At 12 months follow-up, M (SD) Baseline to 12 months difference, M (SD) MISC scores Acceptance (scale 1–7), M (SD) Empathy (scale 1–7), M (SD) MI spirit (scale 1–7), M (SD) MI-consistent behaviors (frequency), M (SD) MI-inconsistent behaviors (frequency), M (SD) Percent MI-consistent, M (SD) Percent open question, M (SD) Percent complex reflection, M (SD) Ratio reflection/question, M (SD) a

pa

1 (n = 33)

2 (n = 26)

3 (n = 9)

4 (n = 21)

5 (n = 6)

37.4 (17.1) 75.8 48.5 54.5 0.5 (1.2)

41.4 (15.2) 96.2 65.4 69.2 1.1 (0.9)

41.0 (23.6) 55.6 88.9 44.4 0.3 (0.4)

36.0 (17.6) 81.0 76.2 57.1 0.2 (1.1)

34.9 (19.2) 83.3 83.3 33.3 −0.1 (0.9)

.80 .08 .08 .46 .02

13.3 (9.6) 10.8 (9.0) −2.4 (8.2)

12.7 (8.2) 11.8 (10.0) −0.9 (10.8)

9.6 (7.7) 8.2 (4.4) −1.4 (7.8)

13.9 (11.0) 7.0 (7.2) −6.9 (10.3)

18.1 (17.3) 31.5 (29.7) 13.4 (19.6)

.60 b.001 b.01

6.0 (0.6) 5.9 (0.3) 5.7 (0.4) 38.2 (12.0) 0.9 (1.4) 97.9 (2.4) 62.1 (9.3) 46.3 (10.1) 1.1 (0.4)

5.6 (0.6) 5.1 (0.5) 5.2 (0.5) 31.5 (8.4) 1.7 (1.5) 95.0 (4.2) 41.4 (8.6) 46.6 (11.4) 0.8 (0.2)

5.6 (0.4) 4.7 (0.4) 5.1 (0.4) 22.6 (6.1) 1.3 (0.8) 94.7 (2.7) 58.0 (10.6) 40.2 (6.8) 0.9 (0.3)

6.5 (0.4) 6.0 (0.3) 6.0 (0.2) 28.2 (6.4) 0.3 (0.6) 99.0 (1.7) 49.6 (10.2) 53.4 (11.2) 1.6 (0.4)

4.8 (0.9) 4.8 (0.9) 4.3 (0.9) 31.1 (9.6) 2.9 (2.7) 91.4 (6.5) 54.9 (14.5) 24.8 (10.2) 0.7 (0.2)

b.001 b.001 b.001 b.001 b.01 b.001 b.001 b.001 b.001

Pearson's chi-squared test for categorical variables and ANOVA for continuous variables.

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Table 2 Mixed models estimating the effect of counselor skills on the link between patient-perceived ability to change during BMI and alcohol outcome (baseline to 12 months follow-up difference in weekly drinking amount) Skills Acceptance Constant (γ00) Acceptance (γ01) Ability to change (γ10) Interaction Acceptance × Ability to change (γ11) Empathy Constant (γ00) Empathy (γ01) Ability to change (γ10) Interaction Empathy × Ability to change (γ11) MI spirit Constant (γ00) MI spirit (γ01) Ability to change (γ10) Interaction MI spirit × Ability to change (γ11) MI-consistent behaviors frequency Constant (γ00) MI-consistent behaviors frequency (γ01) Ability to change (γ10) Interaction MI-consistent behaviors × Ability to change (γ11) MI-inconsistent behaviors frequency Constant (γ00) MI-inconsistent behaviors frequency (γ01) Ability to change (γ10) Interaction MI-inconsistent behaviors × Ability to change (γ11) Percent MI-consistent Constant (γ00) Percent MI-consistent (γ01) Ability to change (γ10) Interaction Percent MI-consistent × Ability to change (γ11) Percent open questions Constant (γ00) Percent open questions (γ01) Ability to change (γ10) Interaction Percent open questions × Ability to change (γ11) Percent complex reflections Constant (γ00) Percent complex reflections (γ01) Ability to change (γ10) Interaction Percent complex reflections × Ability to change (γ11)

Standard error

p

−61.35 10.49 28.97 −4.47

14.17 2.38 17.75 3.00

b.01 b.01 .10 .14

−44.37 8.15 33.40 −5.51

24.86 4.61 26.08 4.74

.07 .08 .20 .25

−52.86 9.72 27.95 −4.59

13.95 2.52 18.09 3.27

b.01 b.01 .12 .16

4.24 −0.17

21.62 0.70

.84 .81

3.00 0.00

16.41 0.51

.86 .99

8.69 −7.13

2.11 1.61

b.01 b.01

−0.44 2.60

2.61 1.92

.87 .18

−200.26 2.08 90.03 −0.90

58.13 0.60 70.63 0.73

b.01 b.01 .20 .22

10.04 −0.20

25.75 0.48

.70 .68

−11.87 0.27

10.54 0.20

.26 .17

−28.89 0.65

7.08 0.15

b.01 b.01

18.06 −0.35

9.42 0.20

.06 .08

Coefficient

Table 2 (continued) Skills Ratio reflections/questions Constant (γ00) Ratio reflections/ questions (γ01) Ability to change (γ10) Interaction Ratio reflections/questions × Ability to change (γ11)

Coefficient

Standard error

p

−15.82 14.90

4.51 3.89

b.01 b.01

11.59 −7.88

5.48 4.86

.03 .11

ability to change without taking into account counselor skills. This is graphically presented in Fig. 1. Slopes indicate that for Counselors 1, 2, and 3, the more the patients expressed ability to change, the better the outcome. The fitted line for Counselor 5 shows the poorest outcomes among all counselors for all patients. The flat slope of Counselor 4 at a relatively high level indicates that the patients of this counselor maintained good outcomes regardless of their expressed ability to change. Table 2 presents full cross-level models estimating the effect of counselor skills on the link between perceived ability to change by patients during BMI and alcohol outcome. In these models, we introduced counselor skills to explain (a) the different general levels of efficacy (intercepts) and (b) the increase in efficacy (slopes) with patient ability to change. There were strong influences of counselor skills on intercept estimates for acceptance and MI spirit levels, MI-inconsistent behaviors frequency, percent of MIconsistent behaviors, percent of complex reflections, and reflections/questions ratio. Effects were in the expected direction, namely, that MI skills increased the intercept, whereas MI-inconsistent skills decreased it. Level of empathy was in the expected positive direction but failed (p = .08) to reach the common significance level of p b .05. Clearly nonsignificant were frequency of MI-consistent behaviors and percent of open questions. Slopes were generally less influenced by counselor skills (i.e., not significant at p b .05) but were consistent with intercept values (although opposite in sign). Thus, a counselor skill that was positively associated with higher levels of change (intercepts) tended to have flatter slopes. 4. Discussion Although counselors had the same background, training, and patients with similar alcohol use and sociodemographic data at baseline, their use of MI skills during BMI and related outcomes varied widely. Differences were in the expected direction; MI-consistent skills were related to better alcohol outcomes among patients at 1 year follow-up, whereas the use of MI-inconsistent skills resulted in poorer outcomes. Our findings consistently show that counselors with better overall MI performances (i.e., use of consistent skills and avoidance of inconsistent skills) not only achieved better

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outcomes but also demonstrated more efficacy across all levels of patient ability to change. Conversely, counselors having poorer overall MI performances were effective mainly if patients expressed high levels of ability to change. Before further discussing our findings, several caveats and limitations of this study should be considered. First, there were few patients (6–33) in each counselor caseload and only five second-level units, restricting the analyses (i.e., only one skill could be tested at the time). However, because the results were all in the expected direction, we can hypothesize that if more counselors and patients per counselor were added, this should lead to even more significant results (assuming that the direction of effects does not change). Clearly, we view the results herein as a first step toward testing the effects of counselor skills on the link between patient ability to change and positive behavior changes. This study also shows that such models could be implemented easily. Despite the limited statistical practicability of the current models, this study has led to some extremely meaningful and useful findings but obviously needs replication with a larger sample of counselors, each having more patients. Second, ability to change was measured during, not before, the BMI intervention and could have been influenced by interactions between patient and counselor. Still, this would not necessarily invalidate the finding that counselors having better outcomes in their patients are those having better MI skills. In addition, Counselor 2 showed the only significant mean difference in patient ability to change compared to other counselors was only moderately efficacious and showed a medium slope. Thus, the lack of measuring patient ability to change before BMI cannot explain the slope differences between the least and the most effective counselors (4 and 5). The present results are consistent with previous findings of counselor influence on outcome (Luborsky et al., 1997; McLellan et al., 1988; Najavits & Weiss, 1994; Project MATCH Research Group, 1998). Because counselors in this study all had the same background and training but differed in skills and efficacy, these results help confirm that counselor background and formal education are unrelated to performance differences (McLellan et al., 1988; Najavits & Weiss, 1994). Another interesting finding is the lack of significance in multilevel models for the frequency of MI-consistent skills, whereas MI-inconsistent skills and the percentage of MIconsistent skills performed as good predictors. This might mean that efficacy depends more on the avoidance of MIinconsistent skills than on the absolute frequency of using MI-consistent skills. Thus, using MI-consistent skills in abundance is not particularly helpful if accompanied by frequent use of MI-inconsistent skills. This conclusion is consonant with the observation of Miller et al. (1993) that one MI-inconsistent skill (confrontation) was the single therapist behavior predictive of negative outcome. An implication of this finding is that training in MI and BMI should focus more

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strongly on the avoidance of this and other MI-inconsistent skills, such as warning or giving advice without patient permission, than on using MI-consistent skills. In a recent study, we analyzed single counselor behaviors (e.g., affirm, confront, etc.) and determined that these did not predict changes in alcohol use (Gaume et al., 2008). In the present findings, summary scores (e.g., percent of MIconsistent behaviors) of MI skills nested within counselors did predict alcohol use changes in the expected direction. This could mean that the use of any one MI skill by itself is neither sufficient nor necessary to produce favorable outcomes; the gestalt of a counselor possessing an overall “MI attitude” combining acceptance, MI spirit, confrontation, and warning avoidance, complex reflective listening use, and more reflecting than asking, seems to be more important in producing beneficial outcomes among most patients. Also of importance is the finding that slope differences indicate that counselors with better MI skills showed efficacy across all levels of patient ability to change, whereas counselors with poorer MI skills were efficacious mostly with patients expressing high levels of ability to change. If counselor efficacy is linked only to patient ability to change, the sole benefit of BMI would be in providing environments where patients can begin to think and talk about change and receive counselor reinforcement. On the other hand, if efficacy is not particularly linked to patient ability to change, it would imply that counselors could influence behaviors of all patients during BMI. Because counselors with good MI skills are the ones positively affecting most patients, our results clearly emphasize the usefulness of MI techniques and spirit during brief interventions targeting behavior change. Because most counselors were particularly effective with patients having high levels of ability to change and because patient ability to change was globally low in this study, these conditions help explain the overall lack of efficacy in the larger study (Daeppen, Gaume, et al., 2007). Such results are comparable to those of others (Luborsky et al., 1986), who observed considerable variability in outcome within the caseload of individual therapists and demonstrated that the size of therapist effects generally was more important than the form of the treatment. Our findings are also in accordance with findings of another research project (Project MATCH Research Group, 1998). Within one of their trials, client outcomes were only modestly affected by the treatment condition to which they were assigned. The authors examined therapist differences and found significant effects in client satisfaction and outcomes. However, although important outcome differences were found among therapists, the authors could not achieve clarity for these by any specific attributes of the therapists (such as personality traits or characteristics) and concluded that outcome variance might be more closely tied to therapist behaviors during treatment. Our findings appear to offer confirmation of this. Our results may also provide an answer to those who are skeptical about the efficacy and usefulness of minimal, short,

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single interventions (e.g., Saitz et al., 2006). Taking the entire sample and comparing it to a control group (Daeppen, Gaume, et al., 2007), our single 15-minute intervention does not seem very effective. However, this study suggests that counselors who are not only trained but who also have adopted an actual MI attitude were most efficacious. This suggests that counselors should still be trained and supervised adequately, but might be selected for their propensity or “natural” facility to adhere to MI spirit and techniques to achieve optimal results when placed in research projects or in behavior change counseling settings. This is in accord with recommendations from another source (Miller, Moyers, Arciniega, Ernst, & Forcehimes, 2005). These authors described the training, supervision, quality monitoring, and assessment of counselor qualifications to guarantee consistency in treatment delivery. They suggest systematic behavioral performance prescreening of counselor clinical skillfulness as a possible prerequisite for entry to training for future studies, including those within specific care settings. If this study were to be replicated using larger sample sizes, it might show that using an optimal combination of MI skills predicts more beneficial outcomes among patients, independent of their perception or expression of ability to change, and that counselor training should focus more on developing a general MI-consistent gestalt of counseling that places more emphasis on avoiding the use of MIinconsistent behaviors than on the frequent use of MIconsistent behaviors. Acknowledgments This study was funded by the Swiss National Science Foundation (Grant 3200-067949). We would like to thank Frank Duc, who participated in the coding for the interventions, and Dr George Danko, PhD, for the careful editing of the manuscript. This study was presented as an abstract at the First International Conference on Motivational Interviewing, Interlaken, Switzerland, June 9–11, 2008, and at the 2008 Research Society on Alcoholism and International Society for Biomedical Research on Alcoholism Joint Meeting, Washington, DC, June 28–July 2, 2008. References Babor, T. F., Caetano, R., Casswell, S., Edwards, G., Giesbrecht, N., Graham, K., et al. (2003). Alcohol: No ordinary commodity. Research and Public Policy. Oxford. Baer, J. S., Rosengren, D. B., Dunn, C. W., Wells, E. A., Ogle, R. L., & Hartzler, B. (2004). An evaluation of workshop training in motivational interviewing for addiction and mental health clinicians. Drug and Alcohol Dependance, 73, 99−106. Bertholet, N., Daeppen, J. B., Wietlisbach, V., Fleming, M., & Burnand, B. (2005). Reduction of alcohol consumption by brief alcohol intervention in primary care: Systematic review and meta-analysis. Archives of Internal Medicine, 165, 986−995. Bien, T. H., Miller, W. R., & Tonigan, J. S. (1993). Brief interventions for alcohol problems: A review. Addiction, 88, 315−335.

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