AMA journal of ethics最新文献

Pain experienced during gynecologic exams and procedures is dismissed, not recognized, and undertreated by some clinicians. This article considers how duration and intensity of pain experienced can be used to direct care. This article also discusses possible consequences of undertreating pain and suggests pain management standards that can be used by clinicians to provide individualized, trauma-informed care and promote shared decision-making.

Office-based gynecologic procedures (OBGPs) are reimbursed at lower rates than similar office urology and dermatology procedures. But there is a broader "hidden curriculum" in health professions training that perpetuates clinicians' and organizations' acceptance of these patterns of poor reimbursement, disincentivizes research on improving OBGP pain management, and exacerbates tolerance of poor control of patients' OBGP pain. This article suggests strategies for equitable reimbursement that would also likely motivate better, more equitable OBGP pain control.

Patients should receive appropriate pain relief when undergoing procedures. This article canvases historical and sociological underpinnings of how clinicians have responded and should respond in the moment to patients' pain during elective gynecologic procedures, such as intrauterine device placement and first-trimester abortion. This article then considers evidence-based techniques for responding to patients' pain expressions and experiences during such procedures. Finally, this article addresses the nature and scope of clinicians' obligations to respond in the moment to patients' needs when complete pain relief might not be possible.

Datasets are often considered "ideal" when they are large and contain longitudinal and representative data. But even research that uses ideal datasets might not generate high-quality evidence. This article emphasizes the roles that transparency plays in enhancing observational epidemiological findings' credibility and relevance and argues that epidemiological research can produce high-quality evidence even when datasets are not ideal. This article also summarizes strategies for bolstering transparency in key phases of research planning and application.

Data quality for and about American Indian/Alaska Native (AI/AN) people is undermined by deeply entrenched, colonial practices that have become standard in US federal data systems. This article draws on cases of maternal mortality and COVID-19 to demonstrate the ethical and clinical need for inclusive, diverse, and accurate data when researching AI/AN health trends. This article further argues that epidemiologists specifically must challenge implicit bias, question methods and practices, and recognize colonial, racist reporting practices about AI/AN people that have long undermined data collection, analytical, and dissemination practices that are fundamental to epidemiological research.

Historical precursors of the field we now call epidemiology date back to Hippocrates. Modern epidemiological science, however, developed as domestic and international infectious disease transmission accompanied industrialization, some nations' economic growth, and colonial powers' military expansion and dominance. This article canvasses ways in which modern epidemiology influenced public health innovation from the late 18th century through the mid-19th century. Specifically, this article suggests which lessons can be gleaned from political dimensions of epidemiology's history and applied to orientations to medicine and public health today.

In epidemiology, bias is defined as systematic deviation from the truth, and it can arise at different stages of scientific investigation (eg, data collection, methodological application, and outcomes analysis). Epidemiological bias can appear as a consequence of data bias (usually categorized as selection bias or information bias) or social bias (prejudice). Such forms of bias may occur separately or together. This article explores what health professions students should learn about the relationship between data bias and social bias-generated by racial, ethnic, gender, or other kinds of prejudice, singly or in combination-as a source of ethical and clinical concern in health care practices and policies that influence patient care and community health.

The COVID-19 pandemic changed public awareness of the importance of high-quality race and ethnicity data for identifying and redressing widely documented racial and ethnic health inequity. This article emphasizes the importance of high-quality race and ethnicity data in health equity research, as highlighted by the COVID-19 pandemic. The article defines what constitutes high-quality race and ethnicity data, discusses challenges in using these data, and provides 2 cases that illustrate the role of these data in identifying and redressing health inequity. Finally, this article advocates for the use of accurate, standardized, and granular data and highlights the need for community engagement and trust building to improve data quality and research outcomes.