Why Does Employment Discrimination Persist against People with Mental Illness? Effects of Negative Stereotypes, Power, and Differential Discrimination

Mental illness affects a sizable minority of Americans at any given time, yet many people with mental illness (hereafter PWMI) remain unemployed or underemployed relative to the general population. Research has suggested that part of the reason for this is discrimination toward PWMI. This research investigated mechanisms that affect employment discrimination against PWMI. Drawing from theories on stigma and power, three studies assessed 1) the stereotyping of workers with mental illness as unfit for workplace success, 2) the impact of positive information on countering these negative stereotypes, and whether negatively-stereotyped conditions elicited discrimination; and 3) the effects of power on mental illness stigma components. I made a series of predictions related to theories on the Stereotype Content Model, illness attribution, the contact hypothesis, gender and mental health, and power. Studies tested predictions using, 1) an online vignette survey measuring attitudes, 2) an online survey measuring responses to fictitious applications for a middle management position, and 3) a laboratory experiment in which some participants were primed to feel powerful and some were not. Results of Study 1 demonstrated that PWMI were routinely stigmatized as incompetent, dangerous, and lacking valued employment attributes, relative to a control condition. This was especially evident for workers presented as having PTSD from wartime service and workers with schizophrenia, and when the worker was a woman. Study 2 showed that, although both war-related PTSD and schizophrenia evoke negative stereotypes, only schizophrenia evoked hiring discrimination. Finally, Study 3 found no effect of being primed to feel powerful on stigmatizing attitudes toward a person with symptoms of schizophrenia. Taken together, findings suggest that employment discrimination towards PWMI is driven by negative stereotypes; but, stereotypes might not lead to actual hiring discrimination for some labeled individuals.

POSITIVE FILTERED P_N METHOD FOR LINEAR TRANSPORT EQUATIONS AND THE ASSOCIATED OPTIMIZATION ALGORITHM

We propose a positive, accurate moment closure for linear kinetic transport equations based on a filtered spherical harmonic (FP_N) expansion in the angular variable. The FP_N moment equations are accurate approximations to linear kinetic equations, but they are known to suffer from the occurrence of unphysical, negative particle concentrations. The new positive filtered P_N (FP_N+) closure is developed to address this issue. The FP_N+ closure approximates the kinetic distribution by a spherical harmonic expansion that is non-negative on a finite, predetermined set of quadrature points. With an appropriate numerical PDE solver, the FP_N+ closure generates particle concentrations that are guaranteed to be non-negative. Under an additional, mild regularity assumption, we prove that as the moment order tends to infinity, the FP_N+ approximation converges, in the L2 sense, at the same rate as the FP_N approximation; numerical tests suggest that this assumption may not be necessary. By numerical experiments on the challenging line source benchmark problem, we confirm that the FP_N+ method indeed produces accurate and non-negative solutions. To apply the FP_N+ closure on problems at large temporal-spatial scales, we develop a positive asymptotic preserving (AP) numerical PDE solver. We prove that the propose AP scheme maintains stability and accuracy with standard mesh sizes at large temporal-spatial scales, while, for generic numerical schemes, excessive refinements on temporal-spatial meshes are required. We also show that the proposed scheme preserves positivity of the particle concentration, under some time step restriction. Numerical results confirm that the proposed AP scheme is capable for solving linear transport equations at large temporal-spatial scales, for which a generic scheme could fail. Constrained optimization problems are involved in the formulation of the FP_N+ closure to enforce non-negativity of the FP_N+ approximation on the set of quadrature points. These optimization problems can be written as strictly convex quadratic programs (CQPs) with a large number of inequality constraints. To efficiently solve the CQPs, we propose a constraint-reduced variant of a Mehrotra-predictor-corrector algorithm, with a novel constraint selection rule. We prove that, under appropriate assumptions, the proposed optimization algorithm converges globally to the solution at a locally q-quadratic rate. We test the algorithm on randomly generated problems, and the numerical results indicate that the combination of the proposed algorithm and the constraint selection rule outperforms other compared constraint-reduced algorithms, especially for problems with many more inequality constraints than variables.