There is a strong consensus that combining the versatility of machine learning with the assurances given by formal verification is highly desirable. It is much less clear what verified machine learning should mean exactly. We consider this question from the (unexpected?) perspective of computable analysis. This allows us to define the computational tasks underlying verified ML in a model-agnostic way, and show that they are in principle computable.
In 2016, unintentional injuries became the third leading cause of death in the United States. In 2018, 54% of 103 672 unintentional injury deaths were due to drug overdoses among adults 19 to 64 years of age. In Georgia, opioid overdose deaths continued to increase, despite a 2014 state law for naloxone use to prevent deaths, and a 2017 amendment for more widespread community use without a prescription. Given these policies, naloxone availability in pharmacies in underserved communities remains unclear. Our objective is to explore naloxone availability in such communities. Three Public Health and Preventive Medicine residents during a social-cultural-behavioral longitudinal rotation conducted interviews of 9 community pharmacists. Several themes emerged: more education was needed, and naloxone was available only by prescription in certain pharmacies or in limited amounts. Additional assessments among community members and sectors can examine the extent to which policies to expand naloxone availability and accessibility are implemented, including reduced naloxone costs.
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