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Introduction At the San Francisco Department of Public Health, Population Health Division, we promote Decision Quality (DQ) [1,2]. DQ starts by knowing what a good decision looks like. A good decision is built with six quality requirements (Table 1). Table 1: Decision quality requirements: A decision is only as strong as its weakest link Name Quality requirements Key question Frame Appropriate frame What are we deciding and why?

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View or download full article: https://escholarship.org/uc/item/2r7298jr Abstract Public health principles are based on promoting dignity, equity and compassion for all. San Francisco has made great strides towards “Getting to Zero” HIV infections, deaths, and stigma. However, we face new challenges with persons who inject drugs (PWID), and increases in substance use disorder, mental illness, and homelessness. Residents and visitors are concerned about (a) an increase in people injecting drugs in public and (b) an increase in discarded syringes on the streets of San Francisco.

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Public health leadership is the practice of mobilizing people, organizations, and communities to effectively tackle tough public health challenges.1 Our population health goals include protecting and promoting equity and health, transforming people and place, ensuring a healthy planet, and achieving health equity. We are all public health leaders, and embodying and promoting equity is our core value. Unless we meaningfully pursue eliminating workplace inequities in all its forms we will be hampered in addressing other inequities, including racial and health.

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Most of what we know about our emotions (and how our brain works) is just plain wrong—and sometimes harmful! Myth: our emotions are hard-wired by evolution in our “limbic” (“reptilian”) brain and emotions are “triggered.” Myth: across the globe humans have universal facial expressions for core emotions (e.g., joy causes smiling). Myth: we control our emotions with our “rational” (neocortex) brain. Myth: the amygdala is the “fear center” of the brain.

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Causal reasoning is at the core of everything we see, do, and imagine. Causal inference is the foundation of scientific thinking and reasoning. Every explicit decision we make is the realization of causal thinking. You will be surprised to learn that the rigorous study of causality as a science is relatively new in comparison to the disciplines of statistics and probability. The history of the Causal Revolution will surprise, inspire, entertain, and—at times—shock you!

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I recently presented to our Board of Supervisors (BOS) about the public health issues related to street cleaning. In San Francisco there is increasing public concern about human defecation, discarded syringes and needles, and homeless encampments.1 2 Not too long ago we had an outbreak of shigellosis among our homeless population.3 In San Francisco we deploy an integrated model for controlling infectious diseases that we developed several years ago. We refined the model based on concepts from transmission dynamics (e.

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Essential population health goals include protecting and promoting equity and health, transforming people and place, ensuring a healthy planet, and achieving health equity. Naturally, I am often asked to give talks on health inequities (see Presentations). My general approach is to connect structural trauma (poverty, racism, discrimination) and toxic (traumatic) stress (adverse childhood experiences), inter-generational transmission of biological and social risk to offspring, life course neurocognitive development affecting a child’s brain, learning, behavior, and health for life, and industry exploitation of our neuro-vulnerabilities to design and market products for addiction and overconsumption (tobacco, alcohol, prescription opioids, processed foods, gambling, gaming, mobile phones, etc.

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In Part 1 we introduced directed acyclic graphs (DAGs) [1] as a better way to represent program theory [2]. A DAG is a Bayesian network where each directed arrow represents a causal link, not merely a probabilistic dependency [3]. Program theory has three components: Theory of causation (causal model) Theory of change (selected strategy) Theory of action (selected intervention that activates change) Program theory is often called “theory of change.

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Introduction As a parent, sometimes I have given overly-cautious advice like: “The only way to guarantee not having a ‘drug problem’ is to not use drugs.” While the statement is true, it is totally useless advice for those who choose to experiment or consume a psychoactive drug with the potential for problem use.1 A better approach is to provide factual, evidence-based guidelines after the “overly-cautious advice.” Fortunately, Fischer et al. provide just that: evidence-based guidelines for lower-risk consumption of cannabis.

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Every public health intervention has a program theory; however, very few can actually describe the program theory supporting their primary programmatic activity or research. Can you? If not, read on. I too could not describe the program theories supporting my own work until I read Funnell Rogers’ book Purposeful Program Theory [1]. It turns out that program evaluators not only live and breath program theory, but they call it by different names: logic model, program logic, theory of change, causal model, results chain, intervention logic, etc.

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