This chapter considers the concept of proximal and distal causes in various health science fields and common reference to the “proximal–distal model” of disease causation. In many cases, this model is used to capture the interaction of social and biological factors, with social factors as distal causes and biological factors as proximal causes. The authors examine Krieger’s (2008) criticism of this framework and suggestion that proximal–distal language confuses causal thinking, reasoning, and distinctions in health science domains. While Krieger (2008) has recommended the elimination of language of proximal and distal causes, Ross and Kendler argue for another alternative, specifically that while the proximal–distal model captures some etiological scenarios, it should not be applied to all cases. They examine three ways in which social and biological causes lead to health outcomes and argue for the value (and possibility) of clear notions of proximal and distal causes.

A 59-year-old man presents to the outpatient neurology clinic with 9 months of progressive weakness in his bilateral lower extremities. He denies any associated pain, stating it started in his legs, worse on the left, then 2 weeks later he noticed weakness in his shoulders. He reports since then it has become progressively more difficult to stand from a chair, ascend stairs, lift his arms over his head, and, most recently, open jars. He claims his arms and legs seem smaller than they used to. His biggest concern is his difficulty using his hands, with which he now has difficulty carrying or holding objects such as grocery bags. He denies any sensory changes, trauma, or other neurological history.

A 61-year-old woman with well-controlled generalized anxiety disorder is brought to the emergency department via ambulance with concern for seizure. Per her husband, 3 days ago she started complaining of a persistent left-sided headache which has progressively worsened. Two days ago she became lethargic and slept all day. Yesterday she started becoming confused and developed a temperature of 38° C, but did not want to get medical care. On the morning of presentation, she was difficult to arouse and had difficulty speaking. Emergency services providers state that enroute to the hospital she developed a chewing-like movement shortly before uncontrollable right-arm rhythmic shaking and fixed right gaze, then a generalized tonic-clonic seizure lasting 2 minutes. Her husband denies her having any prior similar events, weakness/numbness, cognitive issues, weight loss, or seizures.

The Adolescent Discrimination Distress Index (ADDI) is a multigroup fifteen-item self-report measure designed to capture adolescent distress arising from perceived instances of racially motivated discrimination across peer, educational, and institutional contexts. Since its publication, the ADDI has been one of the most widely adopted instruments in the study of ethnic and racial discrimination among adolescents. This chapter offers a review and critical reflection on the contributions of the ADDI over the past twenty years and the potential boundaries it imposes on the field’s understanding of discrimination and associated academic, socioemotional, and behavioral outcomes among racially and ethnically minoritized youth. It concludes with a discussion of continuing challenges and future directions for use of the ADDI that can adequately capture and help reduce the burden of discrimination facing racially and ethnically minoritized youth in the current sociopolitical landscape.

Astronomy is the oldest of the sciences; yet it ranks prominently within modern science. The knowledge and prediction of celestial phenomena have been documented since the Sumerian and Babylonian epoch; that is, since the invention of cuneiform writing. The development of our astronomical knowledge has both intertwined and spurred the development of mathematics.

A 55-year-old woman presents to the emergency department with 2 weeks of daily attacks of sudden sharp stabbing extreme pain over her left cheek. Attacks last only 1–5 seconds and occur seemingly randomly 1–3 times per day; however, they are occasionally triggered by touching the area or by chewing. After each attack, her left cheek has a mild dull pain that resolves within 1–2 minutes. She denies history of migraine, pain in other areas, history of trauma to that area, recent dental work, or other associated symptoms such as tearing or conjunctival injection. During interview, she talks only out of the side of her mouth and states she’s afraid that moving the affected side will trigger an attack.

This chapter situates reflexivity and positionality as central to critical race social work praxis, centering the role of storytelling and counterstorytelling in dismantling dominant narratives. This chapter challenges social work’s historic alignment with neutrality, professionalism, and liberal reform while exposing its entanglement with racism, capitalism, and colonialism. By engaging first-person narratives and counternarratives, the section emphasizes how social workers can critically interrogate power, privilege, and positionality across micro, mezzo, and macro levels of practice. Ultimately, this section invites readers into an ongoing practice of reflexivity, self-disclosure, and communal care, positioning social work as a deeply political and justice-oriented profession.

The subject of Peter Zachar’s chapter is classification, with a focus on psychiatry (of course). He suggests that classificatory choices should be “understood from the perspective of scientific conventionalism.” I agree that there is an important conventional element in current systems for the classification of mental disorders. Rather than seeing this as necessarily a limitation that needs to be overcome, my view is that many scientific theories, including very successful ones, contain conventional elements. (I take Zachar to agree.) In what follows I try to distinguish or disentangle different ways in which a scientific theory can contain conventional elements and to assess the implications of the presence of conventions in psychiatry.

This chapter explores Bayesian optimization and statistics, which provides a flexible framework for balancing prior knowledge about a system with new data. Many optimization techniques are either rooted in Bayesian statistics, or have a Bayesian interpretation. For example, the Tikhonov and LASSO regularized least-squares come from specific prior distributions on the unknown parameters. Bayesian methods are particularly useful when we have small data sets or complex models. They are used extensively for hyperparameter tuning of machine learning models, for experimental design, and for applications in engineering that involve complex partial differential equations. This chapter provides a high-level overview of Bayesian methods in optimization, starting with statistical estimation. Density estimation, regularization, and expectation maximization will be explored here. Estimating a statistical distribution from data is fundamentally an optimization problem, and is the basis of machine learning. The simplest approach is to assume a known distribution and estimate the parameters using optimization. If we include Bayesian prior information, this becomes maximum a posteriori estimation.