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Multi-dimensional Assessment of Outcome: The Analysis of Conditional Independence as an Integrated Statistical Tool to Model the Relationships Between Variables

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Mental Health Outcome Measures

Abstract

The impression of reductionism may overwhelm clinicians when reading scientific reports on the outcome of psychiatric care. What clinicians consider reductionism often corresponds to an attempt by researchers to simplify the complexity of the studies on outcome of care in psychiatry. Such an attitude is evident also in the choice of the indicators of outcome. Morbidity and mortality rates or data on service utilisation have almost exclusively been the indicators of choice for outcome studies, although their limits in reflecting the real outcome of psychiatric care have often been emphasized (Jenkins 1990; Mirin and Namerow 1991; Attkisson et al. 1992; Ruggeri and Tansella 1995,1996; Tansella and Ruggeri 1996). The result of this tendency has been to enlarge the gap between research and clinical practice and to favour the widespread use of treatments of unproven efficacy.

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Biggeri, A., Rucci, P., Ruggeri, M., Tansella, M. (2010). Multi-dimensional Assessment of Outcome: The Analysis of Conditional Independence as an Integrated Statistical Tool to Model the Relationships Between Variables. In: Thornicroft, G., Tansella, M. (eds) Mental Health Outcome Measures. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-80202-7_15

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  • DOI: https://doi.org/10.1007/978-3-642-80202-7_15

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-80204-1

  • Online ISBN: 978-3-642-80202-7

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