Purpose. People vary in their general preferences for more v. less health care, and the validated Medical Maximizing-Minimizing Scale (MMS) reliably measures this orientation. Medical maximizers (people scoring highly on the MMS) prefer to receive more health care visits, medications, tests, and treatments, whereas minimizers prefer fewer services. However, it is unclear how maximizing-minimizing preferences relate to willingness to pursue appropriate health care. We hypothesized that minimizers are at increased risk of rejecting evidence-based high-benefit care and that maximizers are at risk of wanting low-benefit care. Design. In total, 785 US adults recruited through an online panel expressed preferences to receive or forgo a health care intervention in 18 hypothetical scenarios. In 8 scenarios, the intervention was high benefit per evidence-based guidelines. In the remaining 10 scenarios, the intervention was low benefit. We assessed associations between participants’ MMS score and their preferences for medical intervention in each scenario using regression analyses that adjusted for hypochondriasis, health risk tolerance, health status, and demographic variables. Results. MMS score was significantly associated with preferences in all 18 scenarios after adjusting for other variables. The MMS uniquely explained 11% of the variance in preferences for high-benefit care and 29% of the variance in preferences for low-benefit care. Differences between strong minimizers (10th percentile) and strong maximizers (90th percentile) across the 18 scenarios ranged from 5.6 to 32.3 points on a 1 to 100 preference scale. Conclusions. The MMS reliably predicts people’s willingness to pursue appropriate care, both when appropriate care means taking high-benefit actions and when appropriate care means avoiding low-benefit actions. Targeting and tailoring messages according to maximizing-minimizing preferences might increase the effectiveness of both efforts to reduce overutilization of low-benefit services and campaigns to support uptake of high-benefit care.
Decisions about allocation of scarce resources, such as transplant organs, often entail a trade-off between efficiency (maximize total benefit) and fairness (divide resources equally). Three studies using a hypothetical transplant organ allocation scenario examined allocation to groups vs. individuals. Study 1 demonstrates that allocation to individuals is more efficient than allocation to groups. Study 2 identifies a factor that triggers the use of fairness over efficiency: presenting the beneficiaries as one vs. two arbitrary groups. Specifically, when beneficiaries are presented as one group, policy makers tend to allocate resources efficiently, maximizing total benefit. However, when beneficiaries are divided into two arbitrary groups (by hospital name), policy makers divide resources more equally across the groups, sacrificing efficiency. Study 3 replicates this effect using a redundant grouping attribute (prognosis) and finds evidence for a mediator of the grouping effect -the use of individualizing information to rationalize a more equitable allocation decision.
Answers to political sophistication questions are typically tied to theoretical or normative assumptions, which produce given sets of operational guidelines. In this study, I develop an understanding of election specific expertise, conceived of as three distinct dimensions—knowledge, interest, and media exposure. This methodological approach helps provide a richer appreciation of the unique effects of each dimension on the nature, number, and breadth of candidate considerations employed by voters. Results lend support for the overriding claim that sophistication is a critical source of heterogeneity within the American electorate. The classic democratic competency standard of an issue-driven voting public is achieved through a more knowledgeable, interested citizenry. At the same time, knowledge and interest produce divergent influences on particular types of personality-based candidate evaluations while media exposure is most remarkable for its absence of explanatory value.
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