A. Harwood

We examine stigma and how it operates, then develop a novel framework to classify the range of positions that are conceptually possible regarding how stigma ought to be handled from a public health perspective. In the case of weight stigma, the possible positions range from encouraging the intentional use of weight stigma as an obesity prevention and reduction strategy to arguing not only that this is harmful but that weight stigma, independent of obesity, needs to be actively challenged and reduced. Using weight stigma as an illustrative example, we draw on prior theoretical work on stigma mechanisms and intervention strategies to develop a framework for improving the understanding, evaluation, and planning of anti-stigma interventions. This framework has the potential to help public health actors to map out how protest, contact, education, and regulation strategies can be used to reduce direct discrimination, structural discrimination, and internalized stigma.

There are a number of key data-centric questions that must be answered when developing classifiers for operator functional states. “Should a supervised or unsupervised learning approach be used? What degree of labeling and transformation must be performed on the data? What are the trade-offs between algorithm flexibility and model interpretability, as generally these features are at odds?” Here, we focus exclusively on the labeling of cognitive load data for supervised learning. We explored three methods of labeling cognitive states for three-state classification. The first method labels states derived from a tertiary split of trial difficulty during a spatial memory task. The second method was more adaptive; it employed a mixed-effects stress–strain curve and estimated an individual’s performance asymptotes with respect to the same spatial memory task. The final method was similar to the second approach; however, it employed a mixed-effects Rasch model to estimate individual capacity limits within the context of item response theory for the spatial memory task. To assess the strength of each of these labeling approaches, we compared the area under the curve (AUC) for receiver operating curves (ROCs) from elastic net and random forest classifiers. We chose these classifiers based on a combination of interpretability, flexibility, and past modeling success. We tested these techniques across two groups of individuals and two tasks to test the effects of different labeling techniques on cross-person and cross-task transfer. Overall, we observed that the Rasch model labeling paired with a random forest classifier led to the best model fits and showed evidence of both cross-person and cross-task transfer.