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Utility of the health belief model in examining medication compliance among psychiatric outpatients

Author

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  • Kelly, Gerard R.
  • Mamon, Joyce A.
  • Scott, Jack E.

Abstract

This study investigates the relationship between health beliefs and medication compliance among a group of psychiatric outpatients who were prescribed antipsychotic drug regimens. The method of study was an interview with 107 outpatients discharged from two Veterans Administration Medical Centers. The health belief model (HBM) served as an organizing framework to explore the relationships among perceptions of illness severity, susceptibility, benefits and barriers of treatment, cues to action, and medication compliance. The results provide a systematic description of health beliefs reported by psychiatric outpatients. Analyses examine the ability of beliefs to predict compliance and affirm the model's theoretical cogency and appropriateness for use with psychiatric outpatients. Regression analysis showed that 20% of the total variance in compliance could be explained when all components of the HBM were examined together. The study supported the concepts that psychiatric outpatients hold identifiable patterns of health beliefs and attitudes and that the health belief framework functions best when utilized as an integrated model to examine compliance.

Suggested Citation

  • Kelly, Gerard R. & Mamon, Joyce A. & Scott, Jack E., 1987. "Utility of the health belief model in examining medication compliance among psychiatric outpatients," Social Science & Medicine, Elsevier, vol. 25(11), pages 1205-1211, January.
  • Handle: RePEc:eee:socmed:v:25:y:1987:i:11:p:1205-1211
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    Cited by:

    1. Corrigan, Patrick W. & Salzer, Mark S., 2003. "The conflict between random assignment and treatment preference: implications for internal validity," Evaluation and Program Planning, Elsevier, vol. 26(2), pages 109-121, May.
    2. Lawton, Julia & Peel, Elizabeth & Parry, Odette & Douglas, Margaret, 2008. "Shifting accountability: A longitudinal qualitative study of diabetes causation accounts," Social Science & Medicine, Elsevier, vol. 67(1), pages 47-56, July.
    3. Paolo Roma & Merylin Monaro & Laura Muzi & Marco Colasanti & Eleonora Ricci & Silvia Biondi & Christian Napoli & Stefano Ferracuti & Cristina Mazza, 2020. "How to Improve Compliance with Protective Health Measures during the COVID-19 Outbreak: Testing a Moderated Mediation Model and Machine Learning Algorithms," IJERPH, MDPI, vol. 17(19), pages 1-17, October.

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