DEVELOPMENT OF A MEDICAL MAXIMIZER SCALE
Method: 606 participants recruited via Amazon’s Mechanical Turk completed an online survey. Mean age was 33 (SD=11; range=18-80), and the majority (81%) were white. Participants responded to 52 questions designed to assess the 2 proposed preference dimensions (maximizer/minimizer; naturalist/technology enthusiast). An exploratory factor analysis identified items that loaded highly onto factors corresponding to these dimensions. To assess predictive validity, participants reported their healthcare utilization and responded to 4 medical decision scenarios.
Result: Analyses showed that 9 questions captured the maximizer/minimizer distinction (Cronbach’s α=.83), and 8 items captured naturalist orientation (α=.86). No factor corresponded to a general preference for technology.
These preference measures significantly predicted self-reported medical utilization and scenario choices, controlling for demographic factors. Relative to minimizers, maximizers took more medicines (r=0.21), visited the doctor more often in the last year (r=0.36), had more medical scans (r=.010), more overnight hospital stays (r=0.13) and had more lifetime surgeries (r=0.12; all p<0.01). Maximizers were also more likely to choose the more active medical treatments in all of the decision scenarios (all p<0.01). Naturalists were more likely to take herbal remedies and supplements (r=0.52), more likely to have visited an acupuncturist (r=0.15), chiropractor (r=0.12) or healer (r=0.25), and were more likely to refuse medicine prescribed by a doctor (r=0.25; all p<0.01). Naturalist orientation was negatively associated with choosing active treatments for 3 hypothetical scenarios.
Conclusion: We developed and validated a scale that assesses preferences for maximizing vs. minimizing healthcare, and preferences for natural treatments. The maximizer/minimizer distinction, in particular, predicted many aspects of healthcare utilization. We hope that this scale will be used to predict patient outcomes and to better understand real-life medical decisions.