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The health and safety of incarcerated persons and correctional personnel have been prominent in the U.S. news media discourse during the COVID-19 pandemic. Examining changing attitudes toward the health of the incarcerated population is imperative to better assess the extent to which the general public favors criminal justice reform. However, existing natural language processing lexicons that underlie current sentiment analysis (SA) algorithms may not perform adequately on news articles related to criminal justice due to contextual complexities. News discourse during the pandemic has highlighted the need for a novel SA lexicon and algorithm (i.e., an SA package) tailored for examining public health policy in the context of the criminal justice system. We analyzed the performance of existing SA packages on a corpus of news articles at the intersection of COVID-19 and criminal justice collected from state-level outlets between January and May 2020. Our results demonstrated that sentence sentiment scores provided by three popular SA packages can differ considerably from manually-curated ratings. This dissimilarity was especially pronounced when the text was more polarized, whether negatively or positively. A randomly selected set of 1,000 manually scored sentences, and the corresponding binary document term matrices, were used to train two new sentiment prediction algorithms (i.e., linear regression and random forest regression) to verify the performance of the manually-curated ratings. By better accounting for the unique context in which incarceration-related terminologies are used in news media, both of our proposed models outperformed all existing SA packages considered for comparison. Our findings suggest that there is a need to develop a novel lexicon, and potentially an accompanying algorithm, for analysis of text related to public health within the criminal justice system, as well as criminal justice more broadly.

As the long-standing and ubiquitous racial inequities of the United States reached national attention, the public health community has witnessed the rise of "health equity tourism". This phenomenon is the process of previously unengaged investigators pivoting into health equity research without developing the necessary scientific expertise for high-quality work. In this essay, we define the phenomenon and provide an explanation of the antecedent conditions that facilitated its development. We also describe the consequences of health equity tourism - namely, recapitulating systems of inequity within the academy and the dilution of a landscape carefully curated by scholars who have demonstrated sustained commitments to equity research as a primary scientific discipline and praxis. Lastly, we provide a set of principles that can guide novice equity researchers to becoming community members rather than mere tourists of health equity.

Differences in health outcomes across racial groups are among the most commonly reported findings in health disparities research. Often, these studies do not explicitly connect observed disparities to mechanisms of systemic racism that drive adverse health outcomes among racialized and other marginalized groups in the United States. Without this connection, investigators inadvertently support harmful narratives of biologic essentialism or cultural inferiority that pathologize racial identities and inhibit health equity. This paper outlines pitfalls in the conceptualization, contextualization, and operationalization of race in quantitative population health research and provides recommendations on how to appropriately engage in scientific inquiry aimed at understanding racial health inequities. Race should not be used as a measure of biologic difference, but rather as a proxy for exposure to systemic racism. Future studies should go beyond this proxy use and directly measure racism and its health impacts.VISUAL ABSTRACTAppeared as Annals "Online First" article.

Gender-affirming hormones have been shown to improve psychological functioning and quality of life among transgender and nonbinary (trans) people, yet, scant research exists regarding whether and why individuals take more or less hormones than prescribed. Drawing on survey data from 379 trans people who were prescribed hormones, we utilized multivariable logistic regression models to identify factors associated with hormone-dosing behaviors and content analysis to examine the reasons for dose modifications. Overall, 24% of trans individuals took more hormones than prescribed and 57% took less. Taking more hormones than prescribed was significantly associated with having the same provider for primary and gender-affirming care and gender-based discrimination. Income and insurance coverage barriers were significantly associated with taking less hormones than prescribed. Differences by gender identity were also observed. Addressing barriers to hormone access and cost could help to ensure safe hormone-dosing behaviors and the achievement trans people's gender-affirmation goals.

INTRODUCTION The provision of hospital-based obstetrics care in the United States facilitates disparate maternal and perinatal health care experiences and outcomes that disproportionately and unfairly impact Black mothers and birthing [...], PURPOSE We undertook a study to assess whether presence of community support persons (CSPs), with no hospital affiliation or alignment, mitigates acts of obstetric racism during hospitalization for labor, birth, and immediate postpartum care. METHODS We conducted a cross-sectional cohort study, measuring 3 domains of obstetric racism as defined for, by, and with Black birthing people: humanity (violation of safety and accountability, autonomy, communication and information exchange, and empathy); kinship (denial or disruption of community and familial bonds that support Black birthing people); and racism in the form of anti-Black racism and misogynoir (weaponization of societal stereotypes and scripts in service provision that reproduce gendered anti-Black racism in the hospital). We used a novel, validated instrument, the Patient-Reported Experience Measure of Obstetric Racism (the PREM-OB Scale suite), and linear regression analysis to determine the association between CSP presence during hospital births and obstetric racism. RESULTS Analyses were based on 806 Black birthing people, 720 (89.3%) of whom had at least 1 CSP present throughout their labor, birth, and immediate postpartum care. The presence of CSPs was associated with fewer acts of obstetric racism across all 3 domains, with statistically significant reductions in scores in the CSP group of one-third to two-third SD units relative to the no-CSP group. CONCLUSIONS Our findings suggest that CSPs may be an effective way to reduce obstetric racism as part of quality improvement initiatives, emphasizing the need for democratizing the birthing experience and birth space, and incorporating community members as a way to promote the safety of Black birthing people in hospital settings. Key words: childbirth; labor, obstetric; maternal health services; obstetric racism; medical racism; health care disparities; misogynoir; Black maternal health; health inequities; community support person; labor coach; vulnerable populations https://doi.org/10.1370/afm.2958 Annals "Online First" article

'A patient of mine whom I had not yet met,' the white coat ceremony speaker began, 'once walked up to me and said, 'Hey Doc!' When I asked him how [...], Recent controversies over the characteristics of "professionalism" and its enforcement by medical educators underscore the racialized and gendered norms implicit in this practice. In this essay, we describe the ways nebulous definitions of "professionalism" imbue White, cisgender, straight, and able-bodied standards to police the boundaries of belonging in medicine. As such, marginalized trainees remain unfinished sculptures, forced to chisel away dimensions of experience and expression to conform to "professional" standards. We seek to resculpt professionalism in a way that centers patients and trainees currently at the margins. This will strengthen the increasingly diverse workforce and ensure that they can effectively address the needs of patients often excluded from quality care. https://doi.org/10.1370/afm.2892

Accurately measuring gender and sex is crucial in public health and epidemiology. Iteratively reexamining how variables—including gender and sex—are conceptualized and operationalized is necessary to achieve impactful research. Reexamining gender and sex advances epidemiology toward its goals of health promotion and disease elimination. While we cannot reduce the complexities of sex and gender to simply an issue of measurement, striving to capture these concepts and experiences accurately must be an ongoing dialogue and practice—to the benefit of the field and population health. We assert that epidemiology must counteract misconceptions and accurately measure gender and sex in epidemiology. We aim to summarize existing critiques and guiding principles in measuring gender and sex that can be applied in practice. [ABSTRACT FROM AUTHOR]

The article presents an editorial focuses on the need for a trans-led future in trans health research to move away from damage-centered and pathologizing approaches. Topics include the importance of inclusive, intersectional research methodologies, the role of language and terminology in shaping understanding of trans experiences, and the call for submissions that highlight resilience and strengths within trans communities.

Resilience, the individual trait of being able to persist and cope with, often recurrent, negative experiences, has experienced an explosion in recent years as a topic of study. In this commentary, we critique this surge and problematize the co-occurring development of the "resilience as treatment" paradigm. We show that resilience is an expectation foisted primarily on historically and contemporarily oppressed and excluded populations often in response to systemic and structural forms of discrimination. We argue that this represents a fundamental mismatch of intervention and problem; offering an individual-level solution to a structural toxin. In doing so, we re-contextualize resilience as an adverse event, more analogous to scar tissue than a reliable treatment paradigm. Our essay concludes with offering alternatives to resilience that originate with the holistic trauma and liberation health frameworks. These paradigms are united in that, in contrast to resilience, they emphasize healing from structural violence, rather than adapting to it. [ABSTRACT FROM AUTHOR]

Purpose: With the expansion of research utilizing electronic healthcare data to identify transgender (TG) population health trends, the validity of computational phenotype (CP) algorithms to identify TG patients is not well understood. We aim to identify the current state of the literature that has utilized CPs to identify TG people within electronic healthcare data and their validity, potential gaps, and a synthesis of future recommendations based on past studies. Methods: Authors searched the National Library of Medicine's PubMed, Scopus, and the American Psychological Association PsycInfo's databases to identify studies published in the United States that applied CPs to identify TG people within electronic healthcare data. Results: Twelve studies were able to validate or enhance the positive predictive value (PPV) of their CP through manual chart reviews (n = 5), hierarchy of code mechanisms (n = 4), key text‐strings (n = 2), or self‐surveys (n = 1). CPs with the highest PPV to identify TG patients within their study population contained diagnosis codes and other components such as key text‐strings. However, if key text‐strings were not available, researchers have been able to find most TG patients within their electronic healthcare databases through diagnosis codes alone. Conclusion: CPs with the highest accuracy to identify TG patients contained diagnosis codes along with components such as procedural codes or key text‐strings. CPs with high validity are essential to identifying TG patients when self‐reported gender identity is not available. Still, self‐reported gender identity information should be collected within electronic healthcare data as it is the gold standard method to better understand TG population health patterns. [ABSTRACT FROM AUTHOR]