Background: Patients' expectations, as a central mechanism behind placebo and nocebo effects, are an important predictor of health outcomes. Yet, theoretically based generic assessment tools allowing for an integrated understanding of expectations across conditions and treatments are lacking. Based on the preliminary 35-item version, this study reports the development and validation of the Treatment Expectation Questionnaire (TEX-Q), a generic, multidimensional self-report scale measuring patients' expectations of medical and psychological treatments.
Methods: The TEX-Q was developed in a validation sample of n = 251 patients undergoing different treatments using exploratory factor analyses and item analyses, as well as analysis of convergent and divergent validity. Confirmatory factor analysis was conducted in an independent sample of n = 303 patients undergoing cancer treatment. Two-weeks test-retest reliability was assessed in n = 28 psychosomatic outpatients.
Results: Factor analyses revealed six theoretically founded stable subscales. The TEX-Q assesses expectations of treatment benefit, positive impact, adverse events, negative impact, process and behavioural control with a total of 15 items. Results for the subscales and the sum score indicated good internal consistency (α = .71-.92), moderate to high test-retest reliability (r = .39-.76) as well as good convergent validity with regard to other expectation measures (r = .42-.58) and divergent validity with regard to measures of generalized expectations (r < .32) and psychopathology (r < .28).
Conclusions: While further validation is needed, the results suggest that the TEX-Q is a valid and reliable scale for the generic, multidimensional assessment of patients' treatment expectations. The TEX-Q overcomes constraints of ad-hoc and disease-specific scales, while allowing to compare the impact of different expectation constructs across conditions and treatments.
Copyright: © 2023 Shedden-Mora et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.