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Development and validation of the Palliative Care Knowledge Scale (PaCKS)

Published online by Cambridge University Press:  27 December 2016

Elissa Kozlov*
Affiliation:
Department of Psychology, Washington University in St. Louis, St. Louis, Missouri
Brian D. Carpenter
Affiliation:
Department of Psychology, Washington University in St. Louis, St. Louis, Missouri
Thomas L. Rodebaugh
Affiliation:
Department of Psychology, Washington University in St. Louis, St. Louis, Missouri
*
Address correspondence and reprint requests to Elissa Kozlov, Department of Psychology, Washington University in St. Louis, Box 1125, One Brookings Drive, St. Louis, Missouri 63130. E-mail: Elissa.Kozlov@wustl.edu.

Abstract

Objective:

The purpose of this study was to develop a reliable and valid scale that broadly measures knowledge about palliative care among non-healthcare professionals.

Method:

An initial item pool of 38 true/false questions was developed based on extensive qualitative and quantitative pilot research. The preliminary items were tested with a community sample of 614 adults aged 18–89 years as well as 30 palliative care professionals. The factor structure, reliability, stability, internal consistency, and validity of the 13-item Palliative Care Knowledge Scale (PaCKS) were assessed.

Results:

The results of our study indicate that the PaCKS meets or exceeds the standards for psychometric scale development.

Significance of results:

Prior to this study, there were no psychometrically evaluated scales with which to assess knowledge of palliative care. Our study developed the PaCKS, which is valid for assessing knowledge about palliative services in the general population. With the successful development of this instrument, new research exploring how knowledge about palliative care influences access and utilization of the service is possible. Prior research in palliative care access and utilization has not assessed knowledge of palliative care, though many studies have suggested that knowledge deficits contribute to underutilization of these services. Creating a scale that measures knowledge about palliative care is a critical first step toward understanding and combating potential barriers to access and utilization of this life-improving service.

Type
Original Articles
Copyright
Copyright © Cambridge University Press 2016 

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References

REFERENCES

Berinsky, A.J., Huber, G.A. & Lenz, G.S. (2012). Evaluating online labor markets for experimental research: Amazon.com's Mechanical Turk. Political Analysis, 20(3), 351368.Google Scholar
Bentler, P.M. (1990). Comparative fit indexes in structural models. Psychological Bulletin, 107(2), 238246.Google Scholar
Born, W., Greiner, A., Sylvia, E., et al. (2004). Knowledge, attitudes and beliefs about end-of-life care among inner-city African Americans and Latinos. Journal of Palliative Medicine, 7(2), 247256.Google Scholar
Brickner, L., Scannell, K., Marquet, S., et al. (2004). Barriers to hospice care and referrals: Survey of physicians' knowledge, attitudes, and perceptions in a health maintenance organization. Journal of Palliative Medicine, 7(3), 411417.Google Scholar
Buhrmester, M., Kwang, T. & Gosling, S. D. (2011). Amazon's Mechanical Turk: A new source of inexpensive, yet high-quality, data? Perspectives on Psychological Science, 6(1), 35. Available from http://datacolada.org/wp-content/uploads/2014/04/Burhmester-Kwang-Gosling-2011.pdf.Google Scholar
Center to Advance Palliative Care (2014). Palliative care facts and stats. Available from https://media.capc.org/filer_public/68/bc/68bc93c7-14ad-4741-9830-8691729618d0/capc_press-kit.pdf.Google Scholar
Cho, S.J., Cohen, A.S. & Kim, S.H. (2014). A mixture group bifactor model for binary responses. Structural Equation Modeling: A Multidisciplinary Journal, 21(3), 375395.Google Scholar
Clark, L.A. & Watson, D. (1995). Constructing validity: Basic issues in objective scale development. Psychological Assessment, 7(3), 309319. Available from http://www.personal.kent.edu/~dfresco/CRM_Readings/Clark_and_Watson_1995.pdf.Google Scholar
Davis, T.C., Crouch, M.A., Long, S.W., et al. (1991). Rapid assessment of literacy levels of adult primary care patients. Family Medicine, 23(6), 433435.Google Scholar
Dow, L.A., Matsuyama, R.K., Ramakrishnan, V., et al. (2010). Paradoxes in advance care planning: The complex relationship of oncology patients, their physicians, and advance medical directives. Journal of Clinical Oncology, 28(2), 299304. Epub ahead of print Nov 23, 2009. Available from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2815718/pdf/zlj299.pdf.Google Scholar
Edworthy, S., Devins, G. & Watson, M. (1995). The Arthritis Knowledge Questionnaire: A test for measuring patient knowledge of arthritis and its self management. Arthritis and Rheumatism, 38(5), 590600.Google Scholar
Ensor, T. & Cooper, S. (2004). Overcoming barriers to health service access: Influencing the demand side. Health Policy and Planning, 19(2), 6979. Available from http://heapol.oxfordjournals.org/content/19/2/69.long.Google Scholar
Fischer, S., Gozansky, W., Kutner, J., et al. (2003). Palliative care education: An intervention to improve medical residents' knowledge and attitudes. Journal of Palliative Medicine, 6(3), 391399.Google Scholar
Grewal, R., Cote, J.A. & Baumgartner, H. (2004). Multicollinearity and measurement error in structural equation models: Implications for theory testing. Marketing Science, 23(4), 519529. Technical appendices and supplementary tables available from http://www.personal.psu.edu/rug2/GCB%20MKS%20Supplementary%20Technical%20Appendices%20Final.pdf.Google Scholar
Grossman, M. & Kaestner, R. (1997). Effects of education on health. In The social benefits of education. Behrman, J.R. & Stacey, N. (eds.), pp. 69123. Ann Arbor: University of Michigan Press.Google Scholar
Hu, L. & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 155.Google Scholar
Kaiser, H.F. (1960). The application of electronic computers to factor analysis. Educational and Psychological Measurement, 20, 141151.Google Scholar
Kline, T.J.B. (2005). Psychological testing: A practical approach to design and evaluation. Thousand Oaks, CA: Sage Publications.Google Scholar
Koffman, J., Burke, G., Dias, A., et al. (2007). Demographic factors and awareness of palliative care and related services. Palliative Medicine, 21(2), 145153.Google Scholar
Kozlov, E. & Carpenter, B.D. (2015). “What is palliative care?” Variability in content of palliative care informational web pages. The American Journal of Hospice & Palliative Care, pii: 1049909115615566. Epub ahead of print Nov 5.Google Scholar
Li, Y., Bolt, D.M. & Fu, J. (2006). A comparison of alternative models for testlets. Applied Psychological Measurement, 30(1), 321.Google Scholar
Meekin, S., Klein, J., Fleischman, A., et al. (2000). Development of a palliative education assessment tool for medical student education. Academic Medicine, 75(10), 986992.Google Scholar
Morrison, S., Maroney-Galin, C., Kralovec, P., et al. (2005). The growth of palliative care programs in United States hospitals. Journal of Palliative Medicine, 8(6), 11271134.Google Scholar
Muthén, L.K. & Muthén, B.O. (2010). Mplus user's guide: Statistical analysis with latent variables. Users' Guide. Los Angeles: Muthén & Muthén.Google Scholar
National Hospice and Palliative Care Organization (2012). Hospice care in America. Available from http://www.nhpco.org/sites/default/files/public/Statistics_Research/2012_Facts_Figures.pdf.Google Scholar
Ogle, K.S., Mavis, B. & Wyatt, G.K. (2002). Physicians and hospice care: Attitudes, knowledge, and referrals. Journal of Palliative Medicine, 5(1), 8592.Google Scholar
Preacher, K.J., Zhang, G., Kim, C., et al. (2013). Choosing the optimal number of factors in exploratory factor analysis: A model selection perspective. Multivariate Behavioral Research, 48(1), 2856. Available from http://quantpsy.org/pubs/preacher_zhang_kim_mels_2013.pdf.Google Scholar
Public Opinion Strategies (2011). Public opinion research on palliative care: A report based on research by Public Opinion Strategies. Available from https://media.capc.org/filer_public/18/ab/18ab708c-f835-4380-921d-fbf729702e36/2011-public-opinion-research-on-palliative-care.pdf.Google Scholar
Rawson, K., Gunstad, J., Hughes, J., et al. (2010). The METER: A brief, self-administered measure of health literacy. Journal of General Internal Medicine, 25(1), 6771. Available from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2811598/pdf/11606_2009_Article_1158.pdf.Google Scholar
Ross, M., McDonald, B. & McGuinness, J. (1996). The Palliative Care Quiz for Nursing (PCQN): The development of an instrument to measure nurses' knowledge of palliative care. Journal of Advanced Nursing, 23, 126137. Available from http://prc.coh.org/pdf/pcqn.pdf.Google Scholar
Schulman-Green, D., Ercolano, E., Jeon, S., et al. (2012). Validation of the knowledge of care options instrument to measure knowledge of curative, palliative, and hospice care. Journal of Palliative Medicine, 15(10), 10911099. Epub ahead of print Jun 13.Google Scholar
Shipley, W.C. (1940). A self-administering scale for measured intellectual impairment and deterioration. Journal of Psychology: Interdisciplinary and Applied, 9(2), 614.Google Scholar
Stanley, JC. & Hopkins, K.D. (1972). Educational and psychological measurement and evaluation. Englewood Cliffs, NJ: Prentice Hall.Google Scholar
Steiger, J.H. & Lind, J.C. (1980). Statistically based tests for the number of common factors. Handout for a talk presented at the Annual Spring Meeting of the Psychometric Society, Iowa City, Iowa, May 30, 1980. Vol. 758, pp. 424–453. Available from http://www.statpower.net/Steiger%20Biblio/Steiger-Lind%201980.pdf.Google Scholar
Streiner, D.L. (2003). Starting at the beginning: An introduction to coefficient alpha and internal consistency. Journal of Personality Assessment, 80(1), 99103.Google Scholar
Tabachnick, B.G. & Fidell, L.S. (2001). Using mulitvariate statistics, 3rd ed. Needham Heights, MA: Allyn and Bacon.Google Scholar
Terwee, C.B., Bot, S.D., de Boer, M.R., et al. (2007). Quality criteria were proposed for measurement properties of health status questionnaires. Journal of Clinical Epidemiology, 60(1), 3442. Epub ahead of print Aug 24, 2006.Google Scholar
Tucker, L.R. & Lewis, C. (1973). A reliability coefficient for maximum likelihood factor analysis. Psychometrika, 38(1), 110.Google Scholar
Veterans Health Administration (2008). Palliative care consultation teams (VHA Directive 2008–066). Available from http://www1.va.gov/vhapublications/ViewPublication.asp?pub_ID=1784.Google Scholar
Weir, J.P. (2005). Quantifying test–retest reliability using the intraclass correlation coefficient and the SEM. The Journal of Strength and Conditioning Research, 19(1), 231240. Available from http://journals.lww.com/nsca-jscr/Abstract/2005/02000/QUANTIFYING_TEST_RETEST_RELIABILITY_USING_THE.38.aspx.Google Scholar