Research Article Critical Annotation

Research Article Critical Annotation

Research Article Critical Annotation

A critical annotation is more than a summary; it also evaluates the material in terms of its usefulness and quality.

You will write a CRITICAL Annotation for your assignments.

Each will be worth 50 pts.

WRITING A CRITICAL ANNOTATION

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(Guideline & Rubric)

The following is a systematic way of evaluating a quantitative research article.

Prepare your critical annotation with appropriate headings and use APA format. State the complete reference for the research [author, title, journal, pages, and URL (if applicable)]. A complete citation of the article goes at the top of the page, below your heading.

 

Brief Summary (5 pts.)

Write a Summary of the article (limited to one paragraph). The summary involves briefly but accurately stating the key points of the article for a reader who has not read the original article. Research Article Critical Annotation

The following Questions are meant to GUIDE you through the critique. You do not have to answer every question below in the following sections.

Review of Literature & Theoretical Framework (5 pts)

  • Does the literature review make the relationships among the variables explicit or place the variables within a theoretical/conceptual framework? What are the relationships?
  • What gaps or conflicts in knowledge of the problem are identified?
  • Are the references cited by the author mostly primary or secondary sources?
  • Do the researchers’ clinical, substantive, or methodological qualifications and experience enhance confidence in the findings and their interpretation?

Statement of the Problem or Purpose (5 pts)

  • What is the problem and/or purpose of the research study?
  • Does the problem or purpose statement express a relationship between two or more variables? If so, what is/are the relationship(s)? Are they testable?
  • What significance of the problem, if any, has the investigator identified?
  • Are the hypotheses testable?

Population & Sample (5 pts)

  • Was the population identified and described?
  • What type of sampling method is used? Is it appropriate to the design?
  • Does the sample reflect the population as identified in the problem or purpose statement?
  • Is the sample size appropriate? To what population may the findings be generalized? What are the limitations in generalizability?

Research Design (5 pts)

  • What type of design is used?
  • Does the design seem to flow from the proposed research problem, theoretical framework, literature review, and hypothesis?
  • What type(s) of data-collection method(s) is/are used in the study?

Data Collection Instruments & Measurement (5 pts)

  • Are the specific instruments adequately described and were they good choices, given the study purpose and study population?
  • Observational methods: Who did the observing? How were the observers trained to minimize bias? Was there an observational guide? Were the observers required to make inferences about what they saw? Is there any reason to believe that the presence of the observers affect the behavior of the subjects?
  • Physiological measurement: Is a rationale given for why a particular instrument or method was selected? If so, what is it? What provision is made for maintaining the accuracy of the instrument and its use, if any?
  • Interviews: Who were the interviewers? How were they trained to minimize the bias? Is there evidence of any interview bias? If so, what was it
  • Questionnaires: What is the type and/or format of the questionnaire(s) (e.g. Likert, open-ended)? Is (Are) it (they) consistent with the conceptual definition(s)?
  • Does the reliability & validity of each instrument seem adequate? Why?

Data Analysis (5 pts)

  • Were analyses undertaken to address each research question or test each hypothesis?
  • What descriptive or inferential statistics are reported?
  • Were these descriptive or inferential statistics appropriate to the level of measurement for each variable?
  • Are the inferential statistics used appropriate to the intent of the hypotheses?

Conclusions (5 pts)

  • Are the results interpreted in the context of the problem/purpose, hypothesis, and theoretical framework/literature reviewed?
  • Are the generalizations within the scope of the findings or beyond the findings?
  • Do the researchers discuss the implications of the study for clinical practice or further research and are those implications reasonable and complete?

Extras

  • Is the report well written, well organized, and sufficiently detailed for critical analysis?
  • Do the researchers’ clinical, substantive, or methodological qualifications and experience enhance confidence in the findings and their interpretation?
  • Is the report well written, well organized, and sufficiently detailed for critical analysis?

 

  • attachment

    mat_1_1.pdf

    Journal of Substance Abuse Treatment 47 (2014) 307–313

    Contents lists available at ScienceDirect

    Journal of Substance Abuse Treatment

    Regular articles

    Predicting substance-abuse treatment providers’ communication with

    clients about medication assisted treatment: A test of the theories of reasoned action and planned behavior☆ 

    Anthony J. Roberto, Ph.D. a,⁎, Michael S. Shafer, Ph.D. b, Jennifer Marmo, Ph.D. c

    a Hugh Downs School of Human Communication at Arizona State University b School of Social Work and Center for Applied Behavioral Health Policy at Arizona State University c Department of Education, Arizona State University

    a b s t r a c ta r t i c l e i n f o

    ☆ This paper was made possible by Cooperative Agree from the Department of Health and Human Services, Health Services Administration. The opinions expressed those of the authors and no endorsement of the HHS or ⁎ Corresponding author. Tel.: +1 11 480 9654 111.

    E-mail address: anthony.roberto@asu.edu (A.J. Rober

    http://dx.doi.org/10.1016/j.jsat.2014.06.002 0740-5472/© 2014 Elsevier Inc. All rights reserved.

    Article history: Received 13 August 2013 Received in revised form 3 June 2014 Accepted 8 June 2014

    Keywords: Medicated assisted treatment (MAT) Substance-abuse treatment providers Theory of reasoned action Theory of planned behavior

    The purpose of this investigation is to determine if the theory of reasoned action (TRA) and theory of planned behavior (TPB) can retrospectively predict whether substance-abuse treatment providers encourage their clients to use medicated-assisted treatment (MAT) as part of their treatment plan. Two-hundred and ten substance-abuse treatment providers completed a survey measuring attitudes, subjective norms, perceived behavioral control, intentions, and behavior. Results indicate that substance-abuse treatment providers have very positive attitudes, neutral subjective norms, somewhat positive perceived behavioral control, somewhat positive intentions toward recommending MAT as part of their clients’ treatment plan, and were somewhat likely to engage in the actual behavior. Further, the data fit both the TRA and TPB, but with the TPB model having better fit and predictive power for this target audience and behavior. The theoretical and practical implications for the developing messages for substance-abuse treatment providers and other health-care professionals who provide treatment to patients with substance use disorders are discussed. Research Article Critical Annotation

    ment Number 1UR1TI024242 Substance Abuse and Menta in this manuscript are strictly SAMHSA is to be inferred.

    to).

    © 2014 Elsevier Inc. All rights reserved.

    Great strides have been made in the past decade in the efficacious application of pharmacological intervention in the detoxification, treatment, and long-term sobriety of patients experiencing alcohol and illicit drug abuse. Medication-Assisted Treatment (MAT) is a form of pharmacotherapy and refers to the treatment for a substance use disorder that includes a pharmacologic intervention as part of a comprehensive substance abuse treatment plan. Pharmacotherapeutic interventions have been demonstrated efficacious in the treatment of opioid abuse (Knudsen, Ducharme, & Roman, 2007; Weiss et al., 2011), alcohol dependence (Chandreakekaran, Sivaprekash, & Chitraleka, 2001), and cocaine dependence (Carroll et al., 2000). In spite of the growing evidence base, adoption and widespread implementation of MAT has lagged, hampered by a combination of structural, financial, and workforce related issues (Knudsen et al., 2007).

    In contrast to other chronic health conditions, treatment of substance use disorders remains largely a disease treated by counselors, social workers and therapists through a network of community based, non-medically-based treatment agencies. Among surveyed substance abuse treatment facilities, only one-third report

    l

    provision of MAT (National Survey of Substance Abuse Treatment Services, 2008), while the vast majority of primary care physicians report little knowledge of, or attendance to, the treatment of substance use disorders among their patients (Mark et al.; 2003). Confounding this situation are long held social beliefs and attitudes regarding the use of medication to treat substance use disorders, with such beliefs often present among a sizeable group of the professionals serving as addiction providers who are themselves in recovery (Institute of Medicine, 1995, 1997). As evidence of the efficacy of MAT continues to accumulate (Friedmann & Schwartz, 2012), so does the research related to providers’ and clients’ attitudes beliefs, and behaviors, regarding MAT (Forman, Bovassdo, & Woody, 2001; Reickmann, Daley, Fuller, Thomas, & McCarty, 2007). In general, these studies report rather powerful social normative influences mediating what might best be described as neutral to negative attitudes toward MAT.

    Little research exists that explores effective strategies for impacting these attitudes and the corresponding behavioral intentions that providers might have about discussing MAT with their clients. Evidence-based targeted communications and information for pro- viders are needed to facilitate improved openness to MAT efficacy, along with their own professional efficacy in promoting and integrating MAT as part of the treatment and recovery services they provide to their patients. Given the potentially important role previous research seems to assign to attitudes, norms, and efficacy in this area, the theories of

     

     

    308 A.J. Roberto et al. / Journal of Substance Abuse Treatment 47 (2014) 307–313

    reasoned action and planned behavior were selected to guide this inquiry. A discussion of each of these theories follows.

    1. The theory of reasoned action and the theory of planned behavior

    According to the theory of reasoned action (TRA; Ajzen & Fishbein, 1980; Fishbein & Ajzen, 1975), the best predictor of a person’s behavior is their intention to perform or not perform the behavior, and the best predictors of intention are a person’s attitude toward the behavior (i.e., do they feel positively or negatively toward the behavior) and subjective norms (i.e., how they think significant others think they should behave). The theory of planned behavior (TPB; Ajzen, 1985) adds a direct link from perceived behavioral control (i.e., how much influence the person has over the behavior) to both intention and behavior. Notably, the TPB “was made necessary by the original model’s limitations in dealing with behaviors over which people have incomplete volitional control” (Ajzen, p. 181). Thus, Ajzen predicts there should be less difference between the TRA and TPB when the behavior in question is under volitional control. Many factors affect whether someone perceives a behavior under their volitional control, such as time, money, skills, cooperation of others, etc. A visual representation of the TPB is included in Fig. 1. Meta- analyses by Albarracin, Johnson, Fishbein, and Muellerleile (2001) and Downs and Hausenblas (2005) offer consistent support for the ability of these theories to predict behavior.

    While the TRA and TPB are typically used to predict how likely an individual is to engage in a given healthy behavior themselves, research also suggests that they can be used to explain recommendations made to patients by medical practitioners (Millstein, 1996; Perkins et al., 2007; Roberto, Goodall, West, & Mahan, 2010; Taylor, Montano, & Koepsell, 1994; Walker, Grimshaw, & Armstrong, 2001). For example, Millstein (1996) found that both the TRA and TPB accurately predicted primary care physicians’ intentions and behavior to provide STI education to adolescents. However, it should be noted that most of these studies took place more than a decade ago, focused on physicians, and did not include any sort of behavioral measure (i.e., the majority focused on intentions rather than actual behavior). Further, the question remains if the TPB is generalizable to other health professionals such as substance-abuse treatmentproviders. So, it seemsthere is still a need for more current research in this area using different participants, an additional topic, and a behavioral measure. Research Article Critical Annotation

    Among other things, Reickmann et al. (2007) used the TRA to predict substance abuse treatment counselor’s intentions to tell their patients to use each of four different types of MAT (methadone, buprenorphine, clonidine, and ibogaine). Results indicate that attitudes and norms explained between 40 and 71% in intentions in

    Attitudes: Positive or negative evaluation of the behavior.

    Beha What

    Perceived Behavioral Control: Perceived ease or difficulty of adopting behavior.

    Subjective Norms: What you think others think you should do.

    Fig. 1. The theory of reasoned action (Ajzen & Fishbein, 1980; Fishbein & Ajzen, 1975) and th reasoned action. The entire figure with shaded box shows the theory of planned behavior.

    these instances. Similarly, Kelly, Deane, and Lovett (2012) looked at whether the TPB accurately predicted if residential substance abuse workers would make an effort to employ evidence-based practices (EBP) into their treatment of clients. In this study, EPB were defined as, “an approach which integrates the most appropriate clinical information and scientific evidence, with a view to improving psychological interventions and therapeutic relationships, and pro- ducing the best treatment outcomes for clients” (p. 662). Results indicate that attitude, norms, and perceived behavioral control explained 41% of the variance in intentions to use EBP. Notably, neither of these studies included a behavioral measure. Research Article Critical Annotation

    In sum, though previous applications of the TRA and TPB in the health arena have focused primarily on predicting whether individuals engage in healthy behaviors, work by Perkins et al. (2007) suggests that they should provide a solid theoretical framework for health professionals behavior in general, and Millstein (1996), Reickmann et al. (2007), and Kelly, Thompson, and Waters (2006) suggest they might also predict health professionals communication with patients in particular. Thus, the following research questions and hypothesis are advanced:

    RQ1: What are substance-abuse treatment providers’ attitudes, subjective norms, perceived behavioral control, intentions, and behavior regarding recommending medication-assisted treatment as part of their clients’ treatment plan? H1A-B: The (A) TRA and the (B) TPB will accurately predict whether or not substance-abuse treatment providers encouraged their clients to use medication-assisted treatment as part of their treatment plan. RQ2: Does the TPB add to the predictive power of the TRA for this target audience and behavior?

    2. Method

    2.1. Response rate and research participants

    2.1.1. Response rate A link to the survey was sent via email to all 510 individuals who

    were (1) subscribers to an e-newsletter distributed by the Addiction Technology Transfer Center(s) (ATTC), and (2) who identified them- selves as serving in a clinical/direct service role in the provision of substance abuse treatment as counselors, clinical supervisors, or peer recovery specialists. Twenty-eight of these surveys were returned as undeliverable. Response rate was calculated as the number of surveys returned (n = 210) divided by the number of surveys that were sent out and not returned asundeliverable (n = 510 − 28 = 482). Thus, the final response rate is 43.57%.

    vioral Intention: you plan to do.

    Behavior: What you actually do.

    e theory of planned behavior (Azjen, 1991). Note: Non-shaded boxes show the theory of

     

     

    309A.J. Roberto et al. / Journal of Substance Abuse Treatment 47 (2014) 307–313

    2.1.2. Research participants Participants were 210 substance-abuse treatment providers

    reporting an average age of 48 (range = 26 to 76; SD = 11.11) and 14 years of substance abuse treatment experience (M = 13.84, SD = 9.37). In the 30 days immediately preceding the survey, these respondents reported seeing a median of 39 clients (M = 64.34; SD = 104.90). Additional descriptive statistics are provided in Table 1. Taken together, these descriptive statistics suggest that respondents had regular, frequent, and intensive interaction with substance abusing clients. Finally, using the first digit from the ZIP code from the agency for which the participants worked, it was possible to determine that participants from all 10 of the U.S. Postal Service’s general regions of the country completed a survey (range = 5 to 18% per region, M = 10.9% per region). Research Article Critical Annotation

    2.2. Instrumentation

    All TRA and TPB measures were developed using procedures outlined by Ajzen and Fishbein (1980) and Madden, Ellen, and Ajzen (1992); and are similar to items developed by Reickmann et al. (2007) and Kelly et al. (2006). Participants were provided with instructions and a definition of MAT adapted from SAMHSA (2010) before being

    Table 1 Participant demographics.

    Variable %

    Sex Male 35.8 Female 64.2

    Ethnicity Hispanic or Latino/a 9.0 European-American 81.9 African-American 7.8 Native American 5.4 Asian 0.5 Other 4.2

    In recovery Yes 46.0 No 54.0

    Location of work Outpatient treatment facility 63.7 Residential treatment facility 19.6 Correction/criminal justice program 15.2 Hospital/medical facility program 7.4 Other 20.6

    Core work functions Assessing clients 79.4 Developing treatment plans 74.0 Providing individual counseling 77.5 Providing group counseling 66.7 Provide case management 63.2

    Medication-assisted treatment offered Yes, MAT is provided on-site 30.9 Yes, but in partnership with a physician/group 16.2 No 50.5

    MAT organizational support level Very unsupportive 12.3 Unsupportive 10.9 Neutral 26.7 Supportive 29.2 Very supportive 20.8

    Workshops/training about use of MAT to treat substance abuse Yes 88.2 No 11.8

    Self-rating knowledge level of MAT Very low 2.0 Low 20.1 Moderate 37.7 High 27.5 Very high 12.7

    Interest in participation in training using MAT Yes 79.9 No 17.2

    prompted to complete a series of forced-choice questions. The definition read, “This survey asks questions about medication-assisted treatment (sometimes referred to as MAT). For the purposes of this survey, medication-assisted treatment is defined as the use of medications such as suboxone, clonidine, and methadone in combi- nation with counseling and behavioral therapies to provide treatment of substance-use disorders.” Research Article Critical Annotation

    Behavior was assessed with two questions. First, participants were asked, “Do you ever talk to your clients about using medication-assisted treatment as part of their treatment plan?” Response categories were “no” and “yes”. Those who answered “no” were coded as not engaging in the behavior (i.e., engaging in the behavior “0% of the time”). Those who answered “yes” were asked the following contingency question, “In the past 6 months, approximately what percentage of your clients have you spoken to about using medication-assisted treatment as part of their treatment plan?” Response categories for this five-pointitem were, “1–20% of clients, 21–40% of clients, 41–60% of clients, 61–80% of clients, and 81–100% of clients.” Intandem, these twoitemswere combined into a single six-point behavioral measure ranging from “0% of clients” to “81–100% of clients.”

    Behavioral intention [e.g., “I (intend to/plan to) encourage my clients to use medication-assisted treatment as part of their treatment plans in the future.”] and subjective norms [e.g., “Most colleagues who are important to me (think that I should/want me to) encourage my clients to use medication-assisted treatment as part of their treatment plan.”] were each assessed with two items. Perceived behavioral control was assessed using three items (e.g., “I am able to effectively encourage my clients to use medication-assisted treatment as part of their treatment plan.”/“I am capable of effectively encourage my clients to use medication-assisted treatment as part of their treatment plan.”/“It is easy for me to effectively encourage my clients to use medication- assisted treatment as part of their treatment plan.”). Response categories for these three sets of items ranged from 1 (“strongly disagree”) to 5 (“strongly agree”). Finally, attitude was assessed by asking, “To me, encouraging my clients to use medication-assisted treatment as part of their treatment plan is:” followed by three five- point semantic differential items (i.e., “bad–good,” “harmful–helpful,” and “useless–useful”). Alphas for the multi-item measures ranged from .82 to .93 (note: individual alphas and mean item scores for all TRA and TPB measures are included in Table 2).

    The design of this study and the development of the instrument were preceded by a qualitative study involving focus groups of clients receiving medication assisted treatment (Malvini-Redden, Tracy, & Shafer, 2013). Key concepts that emerged from that study, elucidating clients’ perspectives on the value and challenges of using MAT, provided general constructs for this study. Instrumentation followed an iterative process and included review by a national panel of colleagues from the ATTC network and a small pilot study with substance abuse counselors (n = 5). These counselors completed a draft version of the instrument and provided verbal feedback with regard to the clarity and comprehensiveness of the items and response options. Based upon their feedback a number of revisions were made to the instrument before the final version was imple- mented for this study. Research Article Critical Annotation

    2.3. Procedures

    Dillman, Smyth, and Christian’s (2009) tailored-design method was used to guide all data collection procedures, and data collection was conducted by the Institute for Social Science Research (ISSR), an organization dedicated to providing a variety of research and data collection services for the sponsoring University and the surrounding community. A consent form and link to the online survey were distributed by email. Each participant was contacted by e-mail up to three times over a 4-week week period to encourage survey completion (though once a participant completed the survey, they

     

     

    Table 2 Reliability, descriptive statistics, and zero-order correlations for all measured variables.

    α M SD 1 2 3 4 5

    1. Attitude toward encouraging (3 items)

    .93 4.05 .81 –

    2. Social norms toward encouraging (2 items)

    .90 2.92 1.04 .52⁎ –

    3. Perceived behavioral control toward encouraging (3 items)

    .82 3.64 .88 .47⁎ .52⁎ –

    4. Behavioral intention to encourage (2 items)

    .88 3.50 .97 .74⁎ .66⁎ .61⁎ –

    5. Behavior (1 composite item) NA 1.72 1.53 .39⁎ .38⁎ .40⁎ .42⁎ –

    Notes. All variables measured on a 5-point scale, except behavior which was measured on a 6-point scale. Correlations based on one-tailed probability estimates. ⁎ p b .001.

    310 A.J. Roberto et al. / Journal of Substance Abuse Treatment 47 (2014) 307–313

    did not receive subsequent mailings). Individuals who completed the survey before the end date received a $10 gift card to Amazon.com. The research procedures were reviewed and approved as exempt status by the sponsoring University’s Office of Research Integrity and Assurances. Research Article Critical Annotation

    3. Results

    3.1. Data analytic plan

    Structural equation modeling (SEM) was used to test the hypoth- esized relationship between the TRA and TPB variables. A path analysis was conducted using EQS 6.1 software (Bentler, 1995). The data were normal (Mardia’s PK = 0.13) allowing for the maximum likelihood estimation method to be used. Model fit was considered acceptable upon meeting the following conditions: (a) a non-significant chi-square (Jöreskog & Sörbom, 1993)—a perfect connection between theory and the study data would yield a χ2 of zero (Bollen, 1989), (b) a comparative fit index (CFI) greater than .95 (Bentler, 1995), (c) root mean-square- error of approximation (RMSEA) less than .10 (Bentler & Bonnett, 1980), and (d) standardized root mean square residual (SRMR) less than .05 (Bentler & Bonnett, 1980). R2 is examined for each dependent construct to assess predictive power.

    3.2. Descriptive statistics

    Table 2 provides the alphas, means, and standard deviations for all measured variables, as well as the zero-order correlations among all measured variables. In answer to research question 1 (and shown in Table 2), substance-abuse treatment providers had very positive attitudes, neutral subjective norms, somewhat positive perceived behavioral control, and somewhat positive intentions toward recom- mending MAT as part of their clients’ treatment plan, but tended to engage in the actual behavior less than 20% of the time. Correlations demonstrate predicted theoretical relationships at the univariate level. Consistent with the TRA and TPB, attitudes, social norms, and perceived behavioral control were positively and significantly related to behavioral intentions. In addition, as predicted by the TRA and TPB, Research Article Critical Annotation

    Attitudes: Positive or negative evaluation of the behavior.

    Behavio What yo

    Subjective Norms: What you think others think you should do.

    .55

    .37

    .51

    Fig. 2. Path model for TRA (H1A). χ 2(6) = 101.28, p b .001

    both behavioral intentions and perceived behavioral control signifi- cantly correlated in the expected direction with behavior.

    3.3. Measurement model

    Hypothesis 1 stated that (A) the TRA and (B) the TPB would accurately predict whether substance-abuse treatment providers en- courage their clients to use medication-assisted treatment as part of their treatment plan. Specifically, attitudes, subjective norms, behavioral intentions, and behavior were analyzed as measured variables for the TRA (see Fig. 1). Twenty participants were excluded from the analysis due to missing data for at least one variable, leaving 184 participants to test whether the model had adequate fit. Table 2 shows the significant and substantial positive relationships between intentions and behavior, attitudes and intentions, and subjective norms and intentions. To test the hypothesis regarding the overall fit of the TRA, these correlations were then used to compute the path coefficients in the hypothesized TRA path model. All three predicted paths were of sufficient size and achieved standard levels of statistical significance. The path coefficient between attitude and behavioral intention was substantial, β = .55, p (.45 ≤ β ≤ .65) = .95. The coefficient between social norms and behavioral intention was moderate and statistically significant, β = .37, p (.27 ≤ β ≤ .47) = .95. The coefficient between behavioral intentions and behaviors was moderate and significant, r = .40, p (.30 ≤ r ≤ .50) = .95. The overall model fit, however, was only adequate, χ2(6) = 101.28, p b .001, CFI = .74, SRMR = .26, and RMSEA = .30CI = .24–.34. Fig. 2 displays the structural model parameters, as well as the amount of explained variance in intentions (R2 = 64.6%) and behavior (R2 = 15.8%). Research Article Critical Annotation

    The TRA and the TPB share all of the same variables with only the addition of perceived behavioral control in the TPB. The inclusion of perceived behavioral control in the model brings with it two additional paths: one from perceived behavioral control to behavioral intent and another from perceived behavioral control to behavior (see Fig. 1). Both paths were predicted to be positive such that perceived behavioral control should increase both behavioral intent and behavior. Research Article Critical Annotation

    To test the overall fit of the TPB, the correlations from Table 2 were used to compute the path coefficients in the hypothesized TPB path model. All of the TPB predictions that overlapped with the TRA were again supported. The path coefficient between attitude and behavioral intention, β = .48, p (.29 ≤ β ≤ .67) = .95, the coefficient between social norms and behavioral intention, β = .29, p (.10 ≤ β ≤ .48) = .95, and the coefficient between behavioral intentions and behaviors, r = .28, p (.19 ≤ r ≤ .47) = .95, were each moderate to substantial. The addition of perceived behavioral control, however, did significantly change the overall fit of the model: perceived behavioral control did have a moderate effect on behavioral intent, β = .23, p (.04 ≤ β ≤ .42) = .95 and behavior, β = .20, p (.01 ≤ β ≤ .39) = .95. Moreover, the hypothesized TPB model suggested excellent fit, χ2(2) = 4.88, p = .09, CFI = .99, SRMR = .03, and RMSEA = .09CI = .00–.19. Fig. 3 displays the structural model parameters, as well as the amount of explained variance in intentions (R2 = 68.2%) and behavior (R2 = 18.3%). These findings suggest the addition of the direct path between perceived behavioral control and intentions, and perceived

    ral Intention: u plan to do.

    Behavior: What you actually do.

    .40

    , CFI = .74, SRMR = .26, and RMSEA = .30CI = .24–.34.

     

     

    Attitudes: Positive or negative evaluation of the behavior.

    Behavioral Intention: What you plan to do.

    Perceived Behavioral Control: Perceived ease or difficulty of adopting behavior.

    Behavior: What you actually do.

    Subjective Norms: What you think others think you should do.

    .51

    .52

    .48

    .29

    .23

    .28

    .20

    .47

    Fig. 3. Path model for TPB (H1B). χ 2(2) = 4.88, p = .09, CFI = .99, SRMR = .03, and RMSEA = .09CI = .00–.19.

    311A.J. Roberto et al. / Journal of Substance Abuse Treatment 47 (2014) 307–313

    behavioral control and behavior. Thus, in support of H1, tests of both models supported the prediction of whether substance-abuse treatment providers encouraged their clients to use medication-assisted treatment as part of their treatment plan; however, the TPB suggested a stronger fit. Research Article Critical Annotation

    In response to RQ2, two analyses were conducted. First, when poor model fit exists, respecification can occur. Both the Wald test (WT) and Lagrange multiplier test (LMT) were analyzed. The WT deter- mines if parameters should be dropped from the model, whereas the LMT frees parameters. Regardless of which test is utilized, Loehlin (1992) emphasized that caution must be made when respecification of a model occurs; modifications should only occur if they are theoretically defensible or consistent with a substantial body of literature. For this study, the WT revealed no options to improve the model by dropping parameters nor is that option theoretically-sound. The LMT, however, suggested that the changes to be made to the model in order to achieve excellent fit were the inclusion of perceived behavioral control to intention, the covarying of attitudes, subjective norms, and perceived behavioral control, and finally the direction correlation of perceived behavioral control with behavior. This suggested final model was identical to that of TPB. Second, a step- wise regression that in the first step regressed intentions on the core TRA variables and in the second step perceived behavioral control showed a small, but significant increase in the predictive power of the model including perceived behavioral control, R2 change = .038, p b .001. Thus, in answer to RQ2 the TPB adds a small but significant amount of predictive power for this target audience and behavior.1

    1 We were also interested in determining if there were any differences between substance-abuse treatment providers who self-identified as a person in recovery and those who did not on both the means of the TPB variables, and in the fit of the final TPB model. The substance abuse treatment workforce has historically consisted of individuals in recovery and the recent emphasis on Recovery Oriented Systems of Care (ROSC) places a growing emphasis on the incorporation of people in recovery within this workforce. These individuals, many of whom were treated before the emergence of MAT as an evidence based practice, could be expected to have negatively biased perspectives regarding MAT. A series of independent-sample t tests revealed significant difference between these two groups on two of the TRA/TPB variables. Specifically, those who were in recovery (M = 2.71, SD = 1.06) perceived signifi- cantly lower norms to encourage clients to use MAT as part of their treatment plan than those who were not in recovery (M = 3.06, SD = 1.01), t (184) = -2.29, p b .05). Further, those who were in recovery (M = 3.31 SD = 1.04) reported significantly lower intentions to encourage clients to use MAT as part of their treatment plan than those who were not in recovery (M = 3.06, SD = 1.01), t (188) = -2.55, p b .05). Given these two differences, we also ran the final TPB model separately for those in and not in recovery. This did not substantially change the fit of the model. Specifically, the fit indices for the final TPB model (as indicated in the main text and in Fig. 3) were, χ2(2) = 4.88, p = .09, CFI = .99, SRMR = .03, and RMSEA = .09CI = .00-.19 (with 68.2% of the variance in intention and 18.3% of the variance in behavior explained by the model). Whereas the fit indices for those in recovery were, χ2(3) = 6.12, p = .13, CFI = .98, SRMR = .04, and RMSEA = .11CI = .00-.24 (with 66% of the variance in intentions and 18% of the variance in behavior explained by the model), and the fit indices for those not in recovery were χ2(3) = 5.69, p = .10, CFI = .99, SRMR = .05, and RMSEA = .10CI = .00-.22 (with 72.6% of the variance in intentions and behavior 14.5% of the variance in behavior explained by the model). Research Article Critical Annotation

    4. Discussion

    The main goal of this study was to see if the TRA and TPB accurately predicted whether substance-abuse treatment providers encouraged their clients to use MAT as part of their treatment plan. A survey measuring all TRA and TPB variables was sent to 510 substance- abuse treatment providers, and 210 (43.6%) of these providers completed this survey. Results indicated that the data fit both models for this target audience and behavior, and that the TPB added to the explanatory power on encouraging clients to use the MAT.

    One important outcome of the present investigation is that it provides a list of factors that influence substance-abuse treatment providers’ recommendations about the use of MAT. Since substance- abuse treatment providers’ recommendations likely influence the decisions their clients make, they play a particularly important role in the use and success of MAT. The current investigation identifies important concepts to integrate into health communication and community-based interventions targeting substance-abuse treatment providers. These results suggest that interventions targeting substance- abuse treatment providers would be effective if organized along the constructs of the TPB. For example, an intervention might attempt to reinforce already existing positive attitudes toward MAT, or increase subjective norms and perceived behavioral control that currently hover around neutral to be more positive. Given that each substance-abuse treatment provider works with a large number of clients, interventions targeting providers could have a much greater impact on more individuals than those just targeting individual clients. Research Article Critical Annotation

    These results confirm and extend the findings of Reickmann et al. (2007) and Kelly et al. (2006). Consistent with both sets of findings, these results confirm the applicability of TRA as a conceptual model for explaining counselor’s attitudes and intentions, and linking the influence that social norms have upon both. Extending these results, these findings also support the small but important influence that counselor’s perceived behavioral control plays in their intentions, suggesting counselors might see this behavior as somewhat but not completely under their control. The current study also included a behavioral measure while neither Rieckmann et al. nor Kelly et al. did not. It is worth noting that while 80% of the participants reported previous training about MAT, an equivalent proportion also indicated a desire for additional training. As such, these findings underscore the importance of providing substance abuse providers with accurate information and skill building opportunities to enhance their effectiveness in counseling clients to consider the use of MAT in addition to information about the physiological properties of MAT. While lacking direct evidence, these results could reflect providers’ unease in their personal effectiveness to promote and to induce their clients to make use of MAT. Skill building opportunities for providers that focus on the use of motivational interviewing, and other strategies that address clients’ ambivalence in using MAT could

     

     

    312 A.J. Roberto et al. / Journal of Substance Abuse Treatment 47 (2014) 307–313

    provide critical influence in facilitating the broader adoption and implementation of this evidence-based practice.

    4.1. Strengths and limitations

    A key strength of this study is that it is theory-based and extends the scope of the TRA and TPB to a topic (i.e., substance-abuse prevention) and target audience (i.e. substance-abuse treatment providers). Second, our survey was designed using procedures outlined by Ajzen and Fishbein (1980) and Madden et al. (1992), which, in tandem with the high alphas obtained in the present study, both provides a high level of confidence in our measures and allows our results to more accurately be compared to other studies. Also for example, the data were collected using Dillman et al.’s (2009) tailored design method, which was specifically developed to reduce nonresponse error (e.g., by increasing participants’ motivation to respond) and measurement error (e.g., by helping respondents provide more complete, accurate, and precise answers). Further, path analysis was conducted using well-established procedures (Bentler, 1995; Bentler & Bonnett, 1980; Bollen, 1989; Jöreskog & Sörbom, 1993), and with a satisfactory sample size for this type of analysis. Third, we had a relatively large national sample, especially given that substance-abuse treatment providers’ communi- cative behaviors were being studied. Finally, as noted above, these results have important practical implications.

    As with any investigations, some potential limitations must also be acknowledged. The main limitation is that the intention–behavior link was measured retrospectively (i.e., both were measured at the same time as opposed to measuring intentions at one time and behavior at some later time). While not ideal, this is a common and accepted practice in TRA and TPB research, especially when using health care providers as research participants (Albarracin et al., 2001; Perkins et al., 2007). However, now that the TRA and TPB have been shown to be relevant to this topic and target audience, future research should be conducted where the measure of intentions precedes the measure of behavior.

    A second limitation is that this study did not include antecedent measures attitudes, subjective norms, or perceived behavioral control. For example, these theories suggest that (1) behavioral beliefs and outcome evaluations should predict attitudes, (2) normative beliefs and motivation to comply should predict norms, and (3) self-efficacy and controllability should predict perceived behavioral control. Since only a smaller proportion of TRA and TPB research includes measures of these antecedent variables, there is no doubt that understanding the factors that underlie these three variables would provide valuable information for both theoretical and practical reasons. Research Article Critical Annotation

    Of course, the TRA and TPB also have limitations of their own. For example, the TRA is designed to explain behaviors that are under a person’s volitional control. Though the TPB was designed to address this issue to some extent by adding perceived behavioral control, other variables likely also play a role, either directly or indirectly, in such decisions. To illustrate, the behavior ecological models (Hovell, Wahlgren, & Gehrman, 2002) include other variables that might influence behavior at numerous levels. The TRA and TPB take into account many key variables at the individual (such as attitudes and skills) and interpersonal (such as norms) levels, but neither explicitly includes variables that might affect behavior at the organization, community, or public policy levels. Another limitation is that both theories assume humans are rational decision makers, and will only be effective to the extent that this is true for the behavior under investigation.

    5. Conclusions

    The results of this study provide important new information to facilitate the adoption of MAT and extend our knowledge about implementing evidence-based practices in substance abuse treatment

    settings. Our results suggest that developing theory-based interventions using TRA or TPB should be effective in targeting substance abuse treatment providers’ communications with their clients about innova- tive, evidence-based treatment strategies, such as MAT. Future studies designed to change substance-abuse treatment providers’ behavior can built upon these findings by testing the relative influence that knowledge dissemination and skill building strategies in combination with promotional and communication strategies has upon provider’s behavior and behavioral intentions regarding their client communica- tions on MAT or other evidence-based treatment innovations. Research Article Critical Annotation

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    • Predicting substance-abuse treatment providers’ communication with clients about medication assisted treatment: A test of t…
      • 1. The theory of reasoned action and the theory of planned behavior
      • 2. Method
        • 2.1. Response rate and research participants
          • 2.1.1. Response rate
          • 2.1.2. Research participants
        • 2.2. Instrumentation
        • 2.3. Procedures
      • 3. Results
        • 3.1. Data analytic plan
        • 3.2. Descriptive statistics
        • 3.3. Measurement model
      • 4. Discussion
        • 4.1. Strengths and limitations
      • 5. Conclusions
      • References