John combines a huge academic and intellectual reputation in the field of testing and assessment with practical applications experience in a range of blue chips. His work ranges from the investigation of advanced statistical and computational techniques for use in test development, to the UK standardisations of widely used psychometric tests.

BackgroundIncreased access to transportation and information has led to the emergence of more diverse patient choice and new forms of health care consumption, such as medical travel. In order for health care providers to effectively attract patients, more knowledge is needed on the mechanisms underlying decision-making of potential travelers from different countries. A particularly promising method of studying the travelers’ motives is collecting data on social media.ObjectivesThe aim of this study was to test what factors influence decision-making of potential medical travelers and how these factors interact. Based on existing literature, the factors analyzed included quality, cost, and waiting time for 2 procedures varying in invasiveness across 12 different destination countries.MethodsDecision-making patterns were examined using a pilot questionnaire that generated a large amount of data from over 800 participants in 40 countries. Participants indicated their willingness to travel given different scenarios. Each scenario consisted of a combination of several factors. Additionally, participants were asked to indicate the reasons for their choice.ResultsIndividuals display high willingness to travel for medical care when combining all participants and scenarios, travel for care was chosen 66.9% of the time. Among the factors influencing their decisions, quality of the medical procedure abroad was considered most important, and cost was least important as shown by chi-square tests and corresponding odds ratios. Log-linear analyses revealed an interaction between time waiting in the local health care system and type of procedure, whereby time pressure increased the odds of agreeing to travel for the more invasive procedure. The odds of traveling to Europe and the USA were by far the highest, although participants indicated that under certain conditions they might be willing to travel to other medical destinations, such as Asia.ConclusionOur measurements yielded several reliable insights into the factors driving medical decision-making. An essential next step would be to expand these findings with a more encompassing sample and more elaborate statistical modeling.

Medical travel has expanded rapidly in recent years, resulting in new markets and increased access to medical care. Whereas several studies investigated the motives of individuals seeking healthcare abroad, the conventional analytical approach is limited by substantial caveats. Classical techniques as found in the literature cannot provide sufficient insight due to the nested nature of data generated. The application of adequate analytical techniques, specifically multilevel modeling, is scarce to non-existent in the context of medical travel. This study introduces the guidelines for application of multilevel techniques in public health research by presenting an application of multilevel modeling in analyzing the decision-making patterns of potential medical travelers. Benefits and potential limitations are discussed.

John combines a huge academic and intellectual reputation in the field of testing and assessment with practical applications experience in a range of blue chips. His work ranges from the investigation of advanced statistical and computational techniques for use in test development, to the UK standardisations of widely used psychometric tests.

Delay discounting has been linked to important behavioral, health, and social outcomes, including academic achievement, social functioning and substance use, but thoroughly measuring delay discounting is tedious and time consuming. We develop and consistently validate an efficient and psychometrically sound computer adaptive measure of discounting. First, we develop a binary search-type algorithm to measure discounting using a large international data set of 4,190 participants. Using six independent samples ( N = 1,550), we then present evidence of concurrent validity with two standard measures of discounting and a measure of discounting real rewards, convergent validity with addictive behavior, impulsivity, personality, survival probability; and divergent validity with time perspective, life satisfaction, age and gender. The new measure is considerably shorter than standard questionnaires, includes a range of time delays, can be applied to multiple reward magnitudes, shows excellent concurrent, convergent, divergent, and discriminant validity-by showing more sensitivity to effects of smoking behavior on discounting.