Public engagement with scientific evidence in health: A qualitative study among primary-care patients in an urban population

Why do experts disagree? The development of a taxonomy

People are increasingly exposed to conflicting health information and must navigate this information to make numerous decisions, such as which foods to consume, a process many find difficult. Although some consumers attribute these disagreements to aspects related to uncertainty and complexity of research, many use a narrower set of credibility-based explanations. Experts' views on disagreements are underinvestigated and lack explicit identification and classification of the differences in causes for disagreement. Consequently, there is a gap in existing literature to understand the range of reasons for these contradictions. Combining the findings from a literature study and expert interviews, a taxonomy of disagreements was developed. It identifies 10 types of disagreement classified under three dimensions: informant-, information- and uncertainty-related causes for disagreement. The taxonomy may assist with adoption of more effective strategies to deal with conflicting information and contributes to research and practice of science communication in the context of disagreement.

Numeracy and Understanding of Quantitative Aspects of Predictive Models: A Pilot Study

BACKGROUND

The assessment of user preferences for performance characteristics of patient-oriented clinical prediction models is lacking. It is unknown if complex statistical aspects of prediction models are readily understandable by a general audience.

OBJECTIVE

A pilot study was conducted among nonclinical audiences to determine the feasibility of interpreting statistical concepts that describe the performance of prediction models.

METHODS

We conducted a cross-sectional electronic survey using the Amazon Mechanical Turk platform. The survey instrument included educational modules about predictive models, sensitivity, specificity, and confidence intervals (CIs). Follow-up questions tested participants' abilities to interpret these characteristics with both verbatim and gist knowledge. Objective and subjective numeracy were assessed using previously validated instruments. We also tested understanding of these concepts when embedded in a sample discrete choice experiment task to establish feasibility for future elicitation of preferences using a discrete choice experiment design. Multivariable linear regression was used to identify factors associated with correct interpretation of statistical concepts.

RESULTS

Among 534 respondents who answered all nine questions, the mean correct responses was 95.9% (95% CI, 93.8-97.4) for sensitivity, 93.1% (95% CI, 90.5-95.0) for specificity, and 86.6% (95% CI, 83.3-89.3) for CIs. Verbatim interpretation was high for all concepts, but significantly higher than gist only for CIs (p < 0.001). Scores on each discrete choice experiment tasks were slightly lower in each category. Both objective and subjective numeracy were positively associated with an increased proportion of correct responses (p < 0.001).

CONCLUSION

These results suggest that a nonclinical audience can interpret quantitative performance measures of predictive models with very high accuracy. Future development of patient-facing clinical prediction models can feasibly incorporate patient preferences for model features into their development.