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Methods: To test this hypothesis, 144 participants from the San Francisco Bay Area (50% female; 50% European Americans, 50% Chinese Americans; age ranged from 18 to 83, mean = 49 yrs., s.d. = 20 yrs.) completed a measure of ideal affect and actual affect (i.e., the affective states that people actually feel). Participants were also presented with two hypothetical healthcare scenarios: (1) one in which they rated an “exciting” and a “calm” physician according to how likely they would be to select the physician, follow his recommendations; find him knowledgeable; and trust, feel comfortable, and like him, and (2) one in which they rated a stimulant and tranquilizer in terms of how effective they thought the medicine was; how likely they would be to fill the prescriptions and take the medicines as prescribed; and how desirable the medicines were. For each option, we created an overall composite score based on participants' ratings.
Results: Linear regression analyses revealed that ideal HAP significantly predicted ratings of the “exciting” physician (standardized beta = 0.30, p < 0.01) and marginally significantly predicted ratings of the stimulant (standardized beta = 0.17, p = 0.06), controlling for actual HAP. Ideal LAP was not significant correlated with ratings of the calm physician or the tranquilizer.
Conclusions: Our findings support the prediction that ideal affect predicts health judgments, specifically of physicians and medical treatments. Matching physicians and treatments to patients' ideal affect may improve compliance and ultimately improve health outcomes.