Demographic Predictors of Elevated LDL Cholesterol in Type 1 Diabetes: A Cross-Sectional Study.

How effectively preventable cardiovascular disease risk factors such as elevated LDL cholesterol are being mitigated in a real-world U.S. type 1 diabetes population is not well understood, and the demographic factors that are independently associated with elevated LDL cholesterol in this population are not well defined.

Genital Tucking Practices in Transgender and Gender Diverse Patients.

Genital tucking (tucking) is the practice of hiding or minimizing the appearance of one's genitals and gonads. We aimed to better understand the prevalence of tucking and its potential effect on behavior and health. An online questionnaire was distributed to adults with a diagnosis of gender dysphoria or gender incongruence (n = 98).

Evaluating Staff Attitudes, Intentions, and Behaviors Related to Cyber Security in Large Australian Health Care Environments: Mixed Methods Study.

Background: Previous studies have identified that the effective management of cyber security in large health care environments is likely to be significantly impacted by human and social factors, as well as by technical controls. However, there have been limited attempts to confirm this by using measured and integrated studies to identify specific user motivations and behaviors that can be managed to achieve improved outcomes.

Objective: This study aims to document and analyze survey and interview data from a diverse range of health care staff members, to determine the primary motivations and behaviors that influence their acceptance and application of cyber security messaging and controls.

CYBER-AIDD: A novel approach to implementing improved cyber security resilience for large Australian healthcare providers using a Unified Modelling Language ontology.

Purpose: This paper proposes a novel cyber security risk governance framework and ontology for large Australian healthcare providers, using the structure and simplicity of the Unified Modelling Language (UML). This framework is intended to mitigate impacts from the risk areas of: (1) cyber-attacks, (2) incidents, (3) data breaches, and (4) data disclosures.

Methods: Using a mixed-methods approach comprised of empirical evidence discovery and phenomenological review, existing literature is sourced to confirm baseline ontological definitions.