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  • Title: Explanatory models of depression and treatment adherence to antidepressant medication: a qualitative interview study.
    Author: Buus N, Johannessen H, Stage KB.
    Journal: Int J Nurs Stud; 2012 Oct; 49(10):1220-9. PubMed ID: 22621864.
    Abstract:
    BACKGROUND: Adherence to antidepressant medication is a challenging clinical issue, which reduces treatment efficacy: 30-60% of all patients commencing treatment with antidepressants are estimated to stop taking the medication within the first 12 weeks. Patients' personal beliefs about depression and antidepressants are regarded as central influences on adherence. OBJECTIVES: The aim was to gain detailed insight into patients' personal accounts of depression and use of antidepressant medication and to relate these accounts to the patients' self-reported level of adherence. METHODS: In-depth, qualitative interviews of 16 depressed patients one, four, eight and twelve months after hospital discharge supplemented by diagnostic interviews and self-report measures. Kleinman's notion of "explanatory model" was used as the theoretical perspective on the patients' illness narratives. Interview transcripts were analysed thematically with "explanatory models" as the starting point. RESULTS: Patients had ambiguous experiences of depression and antidepressants. Patients explained their illness and the medical treatment in experience-near terms. Explanations of the reasons for depression were psychosocial and biology and medicine were not central. However, taking antidepressant medication was a meaningful part of being admitted to hospital, and the adoption of the rhetoric and practices of biomedicine strengthened patients' sense of control and hope for recovery. If medicine was ineffective, the explanatory models legitimised alternative strategies towards recovery, including non-adherence. CONCLUSIONS: The patients' reasons for adhering to antidepressants included a range of diverse psychosocial issues, and could be regarded as a central part of their common sense illness management.
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