Conectar
To measure the prevalence and incidence of nursing home-acquired pressure injuries in older adults residing in Sri Lankan nursing homes.
Pressure injury prevalence and incidence are indicators of safety and quality of care. A significant portion of the global population has a skin color dominated by the presence of melanin. Yet, the number of nursing home residents with darker skin tones who develop pressure injuries in nursing homes is relatively unknown.
Prospective multisite cohort study conducted in nine nursing homes in Sri Lanka. The sample comprised 210 residents aged ≥60 years old.
Semi structured observations and chart audits were used to gather data from July to October 2023. Head-to-toe visual skin assessment to check for nursing home- acquired pressure injuries, Braden pressure injury risk scale and Fitzpatrick skin tone assessments were conducted on all recruited residents at baseline. All recruited residents were followed-up weekly for 12 weeks until detection of a new pressure injury, death, discharge, or transfer.
Pressure injury point prevalence at baseline was 8.1% (17/210). Cumulative incidence was 17.1% (36/210). Incidence density was 15.8 per 1000 resident weeks. Most nursing home-acquired pressure injuries were located on the ankle at baseline (29.4%; 5/17) and in the follow-up period (27.8%; 10/36). Stage I pressure injuries were most common: 58.8% (10/17) and 44.4% (16/36) at baseline and during follow-up respectively.
About one in six nursing home residents developed a new pressure injury over the 12-week follow-up period. Despite staff and resource constraints, there remains a need to focus on the prevention of pressure injuries in Sri Lankan nursing homes.
Studies on the burden of pressure injuries among darker skin tone nursing home residents are lacking and the current evidence available are predominantly from Western countries. The findings of this study highlight the need of targeted preventive measures for nursing home residents with darker skin tones.
The concept of health equity by design encompasses a multifaceted approach that integrates actions aimed at eliminating biased, unjust, and correctable differences among groups of people as a fundamental element in the design of algorithms. As algorithmic tools are increasingly integrated into clinical practice at multiple levels, nurses are uniquely positioned to address challenges posed by the historical marginalization of minority groups and its intersections with the use of “big data” in healthcare settings; however, a coherent framework is needed to ensure that nurses receive appropriate training in these domains and are equipped to act effectively.
We introduce the Bias Elimination for Fair AI in Healthcare (BE FAIR) framework, a comprehensive strategic approach that incorporates principles of health equity by design, for nurses to employ when seeking to mitigate bias and prevent discriminatory practices arising from the use of clinical algorithms in healthcare. By using examples from a “real-world” AI governance framework, we aim to initiate a wider discourse on equipping nurses with the skills needed to champion the BE FAIR initiative.
Drawing on principles recently articulated by the Office of the National Coordinator for Health Information Technology, we conducted a critical examination of the concept of health equity by design. We also reviewed recent literature describing the risks of artificial intelligence (AI) technologies in healthcare as well as their potential for advancing health equity. Building on this context, we describe the BE FAIR framework, which has the potential to enable nurses to take a leadership role within health systems by implementing a governance structure to oversee the fairness and quality of clinical algorithms. We then examine leading frameworks for promoting health equity to inform the operationalization of BE FAIR within a local AI governance framework.
The application of the BE FAIR framework within the context of a working governance system for clinical AI technologies demonstrates how nurses can leverage their expertise to support the development and deployment of clinical algorithms, mitigating risks such as bias and promoting ethical, high-quality care powered by big data and AI technologies.
As health systems learn how well-intentioned clinical algorithms can potentially perpetuate health disparities, we have an opportunity and an obligation to do better. New efforts empowering nurses to advocate for BE FAIR, involving them in AI governance, data collection methods, and the evaluation of tools intended to reduce bias, mark important steps in achieving equitable healthcare for all.
Transgender and nonbinary young adults (TNB YA) report high rates of depression and more suicidality than their cisgender counterparts. Parental rejection is a known predictor of worse mental health among TNB YA; however, less is known about TNB YA experiences of sibling acceptance-rejection. The purpose of this study was to determine how TNB YA perception of sibling and parental acceptance-rejection are related to TNB YA depression and suicidality.
Cross-sectional.
TNB YA (ages 18–25) who had disclosed their gender identity to an adult sibling were recruited to take part in an online study and completed measures of sibling and parent acceptance-rejection, depression, as well as lifetime and past year suicidality. Stepwise regressions were conducted to evaluate associations between acceptance-rejection and TNB YA depression and suicidality.
The sample consisted of 286 TNB YA (Mage = 21.5, SD = 2.2) who were predominantly White (80.6%) and assigned female sex at birth (92.7%). Each family member's acceptance-rejection was associated with increased TNB YA depression scores when considered independently and combined. Independently, high rejection from each family member was associated with greater odds of reporting most suicidality outcomes. When all family members were considered together, only high rejection from a male parent was associated with four times greater odds of reporting lifetime suicidality. High rejection from both parents was associated with greater odds of reporting past year suicide attempt (OR: 3.26 female parent; 2.75 male parent).
Rejection from family members is associated with worse depression and suicidality, and rejection from male parents may be particularly damaging. Sibling acceptance uniquely contributes to TNB YA's depression symptoms alone and in the context of parental support.
Existing literature suggests that transgender women (TW) may be at high risk for adverse mental health due to stress attributed to combined experiences of stigma and complex social and structural vulnerabilities. Little research has examined how these co-occurring experiences relate to mental health. We aimed to test a theoretically driven conceptual model of relationships between stigma, social and structural vulnerabilities, and mental health to inform future intervention tailoring.
Partial least square path modeling followed by response-based unit segmentation was used to identify homogenous clusters in a diverse community sample of United States (US)-based TW (N = 1418; 46.2% White non-Hispanic). This approach examined associations between latent constructs of stigma (polyvictimization and discrimination), social and structural vulnerabilities (housing and food insecurity, unemployment, sex work, social support, and substance use), and mental health (post-traumatic stress and psychological distress).
The final conceptual model defined the structural relationship between the variables of interest within stigma, vulnerability, and mental health. Six clusters were identified within this structural framework which suggests that racism, ethnicism, and geography may be related to mental health inequities among TW.
Our findings around the impact of racism, ethnicism, and geography reflect the existing literature, which unfortunately shows us that little change has occurred in the last decade for TW of color in the Southern US; however, the strength of our evidence (related to sampling structure and sample size) and type of analyses (accounting for co-occurring predictors of health, i.e., stigma and complex vulnerabilities, reflecting that of real-world patients) is a novel and necessary addition to the literature. Findings suggest that health interventions designed to offset the negative effects of stigma must include anti-racist approaches with components to reduce or eliminate barriers to resources that contribute to social and structural vulnerabilities among TW. Herein we provide detailed recommendations to guide primary, secondary, and tertiary prevention efforts.
This study demonstrated the importance of considering stigma and complex social and structural vulnerabilities during clinical care and design of mental health interventions for transgender women who are experiencing post-traumatic stress disorder and psychological distress. Specifically, interventions should take an anti-racist approach and would benefit from incorporating social support-building activities.
FreshRSS 