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Related Experiment Video

Updated: Jul 6, 2026

Using Visual and Narrative Methods to Achieve Fair Process in Clinical Care

Using Visual and Narrative Methods to Achieve Fair Process in Clinical Care

Published on: February 16, 2011

Applying the Expectancy-Value Model to understand health values.

Xu-Hao Zhang¹, Feng Xie, Hwee-Lin Wee

¹Department of Pharmacy, National University of Singapore, Singapore.

Value in Health : the Journal of the International Society for Pharmacoeconomics and Outcomes Research

|April 17, 2008

Summary

The Expectancy-Value Model (EVM) effectively explains health value variances, identifying key attitudinal attributes. Further refinement of measurement scales could enhance its predictive power for health values.

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Last Updated: Jul 6, 2026

Using Visual and Narrative Methods to Achieve Fair Process in Clinical Care

Using Visual and Narrative Methods to Achieve Fair Process in Clinical Care

Published on: February 16, 2011

Area of Science:

Psychology
Health Economics
Decision Science

Background:

The Expectancy-Value Model (EVM) is a robust psychological framework for predicting attitudes by assessing attitudinal attributes (AAs) and external variables.
Health values, akin to attitudes, can be explored using EVM to understand influencing factors and explain variance.

Purpose of the Study:

To apply the EVM to health values to assess its utility in explaining variance.
To identify underlying factors influencing health values using the EVM framework.

Main Methods:

Focus groups identified key AAs for five distinct health states (EQ-5D codes).
AAs were measured using expectancy-value calculations on 7-point Likert scales; health values via Visual Analog Scales (VAS).
Multiple linear and hierarchical regression analyses examined the explanatory power of AAs and external variables (age, sex, ethnicity, education, etc.).

Main Results:

Four significant AAs were identified: worsening quality of life, family burden, reduced independence, and inability to work/study.
EVM explained up to 62% of health value variance across five health states, with AAs contributing 13-28% individually.
No significant external variables were identified; EVM's explanatory power decreased when analyzing individual health states (8-23%).

Conclusions:

The EVM demonstrates utility in explaining health value variances and identifying significant predictors.
Limitations in the 7-point Likert scale may restrict the accurate measurement of AAs, impacting EVM's explanatory power for smaller variances.
Further research with improved, continuous measurement scales for AAs is recommended to enhance the EVM's ability to explain health values comprehensively.