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A Psychometric Evaluation of the Intention Scale for Providers-Direct Items

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Abstract

This study examined the psychometric properties of the Intention Scale for Providers-Direct Items (ISP-D; 16 items), a questionnaire for assessing therapists’ evidence-based practice attitudes, subjective norms, perceived behavioral control, and behavioral intentions. Participants were community mental health providers from the State of Hawaii. A confirmatory factor analysis provided support for a revised 14-item ISP-D measure that fits the data reasonably well. Subscales of this revised ISP-D demonstrated acceptable to good internal consistency, with the exception of the Perceived Behavioral Control subscale. The majority of convergent validity correlation patterns between the ISP-D and related constructs were significant and in predicted directions.

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Notes

  1. Note that if participants marked more than one primary clinical setting, it was indicated that they did not report a primary clinical setting.

  2. Since the item-level data were skewed and technically ordinal, the same analyses were run with the Mplus “categorical” option, which uses the weighted least square means and variance (WLSMV) adjusted estimator and a polychoric as opposed to Pearson correlation matrix. Notably, this approach does not assume the normality of the indicators. This model produced a negative residual variance (− .002) for one factor. Thus, the factor variance was set to 0, which again produced a non-positive definite covariance matrix. Setting the variance to .01 resulted in an appropriately converging model. This model had similar fit statistics to the continuous model (χ2 (98) = 465.314, RMSEA = 0.133 (0.121–0.146), CFI = 0.907, TLI = 0.886, SRMR = 0.079). Model 2 also fit similarly with categorical indicators (χ2 (71) = 215.443, RMSEA = 0.098 (0.083–0.113), CFI = 0.962, TLI = 0.951, SRMR = 0.061).

References

  1. Shafran R, Clark DM, Fairburn CG, et al. Mind the gap: Improving the dissemination of CBT. Behaviour Research and Therapy. 2009; 47(11): 902-909.

    CAS  PubMed  Google Scholar 

  2. Walrath CM, Sheehan AK, Holden EW, et al. Evidence-based treatments in the field: A brief report on provider knowledge, implementation, and practice. The Journal of Behavioral Health Services & Research. 2006; 33(2): 244-253.

    Google Scholar 

  3. Fixsen DL, Naoom SF, Blasé KA, et al. Implementation research: A synthesis of the literature (FMHI Publication No. 231). Tampa, FL: University of South Florida, Louis de la Parte Florida Mental Health Institute, National Implementation Research Network, 2005.

    Google Scholar 

  4. Hoagwood K, Olin SS. The NIMH blueprint for change report: Research priorities in child and adolescent mental health. Journal of the American Academy of Child & Adolescent Psychiatry. 2002; 41(7): 760-767.

    Google Scholar 

  5. Southam-Gerow MA, Rodríguez A, Chorpita BF, et al. Dissemination and implementation of evidence based treatments for youth: Challenges and recommendations. Professional Psychology: Research and Practice. 2012; 43(5): 527.

    Google Scholar 

  6. Lewis CC, Weiner BJ, Stanick C, et al. Advancing implementation science through measure development and evaluation: a study protocol. Implementation Science. 2015; 10(1): 1.

    Google Scholar 

  7. Beidas RS, Kendall PC. Training therapists in evidence-based practice: A critical review of studies from a systems-contextual perspective. Clinical Psychology: Science and Practice. 2010; 17(1): 1-30.

    Google Scholar 

  8. Jensen-Doss A, Hawley KM. Understanding clinicians’ diagnostic practices: Attitudes toward the utility of diagnosis and standardized diagnostic tools. Administration and Policy in Mental Health and Mental Health Services Research. 2011; 38(6): 476-485.

    PubMed  Google Scholar 

  9. Lewis CC, Fischer S, Weiner BJ, et al. Outcomes for implementation science: an enhanced systematic review of instruments using evidence-based rating criteria. Implementation Science. 2015; 10(1): 1.

    Google Scholar 

  10. Nelson TD, Steele RG. Predictors of practitioner self-reported use of evidence-based practices: Practitioner training, clinical setting, and attitudes toward research. Administration and Policy in Mental Health and Mental Health Services Research. 2007; 34(4): 319-330.

    PubMed  Google Scholar 

  11. Martinez RG, Lewis CC, Weiner BJ. Instrumentation issues in implementation science. Implementation Science. 2014; 9(1): 118.

    PubMed  Google Scholar 

  12. Ajzen I. The theory of planned behavior. Organizational Behavior and Human Decision Processes. 1991; 50(2): 179-211.

    Google Scholar 

  13. Albarracin D, Johnson BT, Fishbein M, et al. Theories of reasoned action and planned behavior as models of condom use: A meta-analysis. Psychological Bulletin. 2001; 127: 142-161.

    CAS  PubMed  PubMed Central  Google Scholar 

  14. Godin G, Kok G. The theory of planned behavior: A review of its applications to health-related behaviors. American Journal of Health Promotion. 1996;11: 87-98.

    CAS  PubMed  Google Scholar 

  15. Limbert C, Lamb R. Doctors’ use of clinical guidelines: Two applications of the Theory of Planned Behaviour. Psychology, Health & Medicine. 2002; 7: 301-310.

    Google Scholar 

  16. Ajzen I. The theory of planned behaviour: Reactions and reflections. Psychology and Health. 2011; 26(9): 1113-1127.

    PubMed  Google Scholar 

  17. Armitage CJ, Conner M. Efficacy of the theory of planned behavior: A meta-analytic review. British Journal of Social Psychology. 2001; 40: 471-499.

    CAS  PubMed  Google Scholar 

  18. Sheeran P, Orbell S. Do intentions predict condom use? Meta-analysis and examination of six moderator variables. British Journal of Social Psychology. 1998; 37: 231–250.

    PubMed  Google Scholar 

  19. Ajzen I, Cote NG. Attitudes and the prediction of behavior. In: WD Crano, R Prislin. Attitudes and Attitude Change. New York, NY: Psychology Press, 2008, pp. 289-311.

    Google Scholar 

  20. Casper ES. The theory of planned behavior applied to continuing education for mental health professionals. Psychiatric Services. 2007; 58(10): 1324-1329.

    PubMed  Google Scholar 

  21. Kelly PJ, Deane FP, Lovett MJ. Using the theory of planned behavior to examine residential substance abuse workers intention to use evidence-based practices. Psychology of Addictive Behaviors. 2012; 26(3): 661-664.

    PubMed  Google Scholar 

  22. Klaybor GR. An application of the Theory of Planned Behavior on clinical social workers’ utilization of the DSM-IV: An exploratory study. Dissertation Abstracts International Section A: Humanities and Social Sciences. 1998; 59(7-A): 2720. (UMI No. AAM9841117).

  23. Aarons GA. Mental health provider attitudes toward adoption of evidence-based practice: The Evidence-Based Practice Attitude Scale (EBPAS). Mental Health Services Research. 2004; 6(2): 61-74.

    PubMed  PubMed Central  Google Scholar 

  24. Aarons GA, Glisson C, Hoagwood K, et al. Psychometric properties and U.S. national norms of the Evidence-Based Practice Attitude Scale (EBPAS). Psychological Assessment. 2010; 22(2): 356-365.

    PubMed  Google Scholar 

  25. Burgess AM, Chang J, Nakamura BJ, et al. Evidence based practice implementation within a theory of planned behavior framework. Journal of Behavioral Health Services & Research. 2017; 44(4): 647-665.

    Google Scholar 

  26. Haynes SN, Richard D, Kubany ES. Content validity in psychological assessment: A functional approach to concepts and methods. Psychological Assessment. 1995; 7(3): 238.

    Google Scholar 

  27. Ajzen I. Frequently Asked Questions. Available online at http://people.umass.edu/aizen/faq.html. Accessed on May 21, 2015.

  28. Higa-McMillan CK, Nakamura BJ, Morris A, et al. Predictors of use of evidence-based practices for children and adolescents in usual care. Administration and Policy in Mental Health and Mental Health Services Research. 2015; 42(4): 373-383.

    PubMed  Google Scholar 

  29. Izmirian SC, Nakamura BJ. Knowledge, attitudes, social desirability, and organizational characteristics in youth mental health services. The Journal of Behavioral Health Services & Research. 2016; 43(4): 630-647.

    Google Scholar 

  30. Lim A, Nakamura BJ, Higa-McMillan CK, et al. Effects of workshop trainings on evidence-based practice knowledge and attitudes among youth community mental health providers. Behaviour Research and Therapy. 2012; 50(6): 397-406.

    PubMed  Google Scholar 

  31. Nakamura BJ, Higa-McMillan CK, Okamura KH, et al. Knowledge of and attitudes towards evidence-based practices in community child mental health practitioners. Administration and Policy in Mental Health and Mental Health Services Research. 2011; 38(4): 287-300.

    PubMed  Google Scholar 

  32. Bollen KA, Liang J. Some properties of Hoelter’s CN. Sociological Methods and Research. 1988; 16: 492-503.

    Google Scholar 

  33. Breckler SJ. Applications of covariance structure modeling in psychology: Cause for concern? Psychological Bulletin. 1990; 107(2): 260-273.

    CAS  PubMed  Google Scholar 

  34. Bentler PM, Chou CP. Practical issues in structural modeling. Sociological Methods and Research. 1987; 16(1): 78-117.

    Google Scholar 

  35. Faul, F, Erdfelder E, Buchner A, et al. Statistical power analyses using G*Power 3.1: Tests for correlation and regression analyses. Behavior Research Methods. 2009; 41: 1149-1160.

    PubMed  Google Scholar 

  36. Green SB, Salkind NJ. Using SPSS for Windows and Macintosh: Analyzing and understanding data. Prentice Hall Press, 2010.

  37. Muthén LK, Muthén BO. Mplus (version 8) [computer software]. Los Angeles, CA: Muthén & Muthén, 2017.

    Google Scholar 

  38. Rhemtulla M, Brosseau-Liard PÉ, Savalei V. When can categorical variables be treated as continuous? A comparison of robust continuous and categorical SEM estimation methods under suboptimal conditions. Psychological Methods. 2012; 17: 354–373.

    PubMed  Google Scholar 

  39. Bentler PM. Comparative fit indexes in structural models. Psychological Bulletin. 1990; 107 (2): 238-46.

    CAS  PubMed  Google Scholar 

  40. Steiger JH. Structural model evaluation and modification: An interval estimation approach. Multivariate Behavioral Research. 1990; 25(2): 173-180.

    CAS  PubMed  Google Scholar 

  41. Tucker LR, Lewis C. A reliability coefficient for maximum likelihood factor analysis. Psychometrika. 1973; 38: 1-10.

    Google Scholar 

  42. Dunn G, Everitt BS, Pickles A. Modelling Covariances and Latent Variables Using EQS. CRC Press, 1993.

  43. Hu LT, Bentler PM. Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal. 1999; 6(1): 1-55.

    Google Scholar 

  44. Browne M, Cudeck R. Alternative ways of assessing model fit. In Bollen KA, Long JS (Eds). Testing Structural Equation Models. Newbury Park, CA: Sage, 1993, pp. 136-159.

    Google Scholar 

  45. Singh R. Does my structural model represent the real phenomenon?: a review of the appropriate use of Structural Equation Modelling (SEM) model fit indices. The Marketing Review. 2009; 9(3): 199-212.

    Google Scholar 

  46. Jöreskog KG, Sörbom D. LISREL 8: Structural Equation Modeling with the SIMPLIS Command Language. Scientific Software International, 1993.

  47. George D, Mallery P. IBM SPSS Statistics 21 Step by Step: A Simple Guide and Reference, 2013.

  48. Perkins MB, Jensen PS, Jaccard J, et al. Applying theory-driven approaches to understanding and modifying clinicians’ behavior: what do we know?. Psychiatric Services. 2007; 58(3): 342-348.

    PubMed  Google Scholar 

  49. Nelson TD, Steele RG. Influences on practitioner treatment selection: Best research evidence and other considerations. The Journal of Behavioral Health Services & Research. 2008; 35(2): 170-178.

    Google Scholar 

  50. Kline R. Principles and Practice of Structural Equation Modeling (3rd ed.). New York, NY: Guilford Press, 2011.

    Google Scholar 

  51. Kenny DA. Correlation and causality. New York, NY: Wiley-Interscience, 1979.

    Google Scholar 

  52. Ajzen I. Constructing a TPB Questionnaire: Conceptual and Methodological Considerations. Available online at http://www.people.umass.edu/aizen/pdf/tpb.measurement.pdf. Accessed on May 21, 2005.

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Authors and Affiliations

  1. Psychology Department, University of Hawai’i at Mānoa, 2530 Dole Street, Sakamaki Hall C400, Honolulu, HI, 96822, USA

    Albert C. Mah MA, Kaitlin A. Hill MA & Brad J. Nakamura PhD

  2. Psychology Department, University of North Texas, Denton, TX, USA

    David C. Cicero PhD

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  1. Albert C. Mah MA
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  2. Kaitlin A. Hill MA
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  3. David C. Cicero PhD
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  4. Brad J. Nakamura PhD
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Correspondence to Albert C. Mah MA.

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Mah, A.C., Hill, K.A., Cicero, D.C. et al. A Psychometric Evaluation of the Intention Scale for Providers-Direct Items. J Behav Health Serv Res 47, 245–263 (2020). https://doi.org/10.1007/s11414-019-09675-3

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