please respond to these 2 posts following the instructions and the rubric: post 1: Bias, Confounding, and Practice Gaps in Rural Diabetes Management A significant practice gap in rural health settings is the underutilization of diabetes self?management education (DSME) and digital health tools among adults with type 2 diabetes. Rural communities often experience limited broadband access, reduced digital literacy, transportation barriers, and fewer local diabetes educators, all of which contribute to poorer glycemic outcomes and delayed detection of complications (CDC, 2022; Hale et al., 2020). Despite compelling evidence supporting DSME and digital interventions, these services remain underused in many rural regions. Awareness of Bias and Confounding in Treating This Population Awareness of selection bias, information bias, and confounding is essential when interpreting epidemiological literature and applying it to rural diabetes care. Selection bias may occur when DSME or tele-health studies disproportionately include urban, motivated, or digitally literate participants. This can make interventions appear more effective than they may be for rural adults who face greater access barriers (Rothman et al., 2021). Recognizing this helps clinicians avoid overestimating the feasibility of digital interventions in low?resource settings. Information bias such as inaccurate self?reported diet, glucose monitoring, or incomplete EHR data can distort associations between DSME participation and glycemic control (Friis & Sellers, 2021). Awareness of this bias encourages clinicians to prioritize objective measures and ensure accurate documentation in their own practice. Confounding variables such as socioeconomic status, comorbidities, or access to care may falsely strengthen or weaken observed associations if not properly controlled (Gerstman, 2022). Understanding confounding helps clinicians tailor interventions to the specific needs and barriers of rural populations rather than assuming uniform effectiveness across settings. Strengthen Study Design to Reduce Selection Bias Researchers can minimize selection bias by using population?based sampling, random sampling, or stratified sampling to ensure representation across geographic and socioeconomic groups. Recruiting participants from multiple rural and urban settings improves generalizability and reduces systematic differences between those included and excluded (Rothman et al., 2021). Use Analytical Methods to Control Confounding Confounding can be minimized through multivariable regression, propensity score matching, or stratified analyses. These methods adjust for variables such as income, comorbidities, or access to care, allowing researchers to isolate the true effect of DSME or digital health interventions (Gerstman, 2022). Effects of Bias if Not Minimized If selection bias, information bias, or confounding are not addressed, study results may be misleading. Interventions may appear more effective or less effective than they truly are, leading to inappropriate clinical decisions. Poorly controlled bias can also reduce generalizability, resulting in interventions that do not translate well to rural populations. Ultimately, unaddressed bias can contribute to misallocation of resources and worsening health disparities, particularly for rural adults with type 2 diabetes (Friis & Sellers, 2021; Rothman et al., 2021). References Centers for Disease Control and Prevention. (2022). National diabetes statistics report, 2022. U.S. Department of Health and Human Services. https://www.cdc.gov/diabetes/data/statistics-report/index.html Friis, R. H., & Sellers, T. A. (2021). Epidemiology for public health practice (6th ed.). Jones & Bartlett Learning. Gerstman, B. B. (2022). Epidemiology kept simple (4th ed.). Wiley. Hale, N. L., Bennett, K. J., & Probst, J. C. (2020). Diabetes care and outcomes in rural populations: A review. Journal of Rural Health, 36(2), 180–188. https://doi.org/10.1111/jrh.12345 Rothman, K. J., Greenland, S., & Lash, T. L. (2021). Modern epidemiology (4th ed.). Wolters Kluwer. post 2: Blog 6 Epidemiology The selected practice gap is centered on the lack of accessible and equitable care for individuals with attention-deficit/hyperactivity disorder (ADHD), representing minority and/or marginalized communities. This gap presents itself through inadequate continuity of care, underdiagnosis, delayed diagnostics, and limited accessibility to specialty mental health services (Cenat et al., 2021). Disparities in ADHD assessment and treatment are now enabled by structural barriers, such as cultural stigma, provider bias, socioeconomic limitations, limited insurance coverage, etc. (Baweja et al., 2021). Thus, epidemiologic data should guide nursing practice and inform professionals and decision-makers about persisting inequities in care delivery. Awareness of bias is important for conducting research and applying evidence-based interventions to develop relevant interventions to care for marginalized ADHD individuals (McKenna et al., 2023). With the lack of awareness comes disproportionate inclusion of marginalized individuals into ADHD studies, resulting in interventions mostly targeting people with better-accessible care, the majority populations, and those with higher socioeconomic status, leading to underrepresentation of minority individuals with ADHD (Curley, 2024; Enzenbach et al., 2019). Information bias is also essential for ADHD diagnoses and outcomes based on caregiver reports and self-reported data because ADHD and the associated behaviors can be misclassified due to the impaired reporting accuracy affected by cultural differences, mistrust in the healthcare system, and stigma (Friis & Sellers, 2021; Khalili et al., 2021). It is important to control such issues as social outcomes, trauma exposure, poverty, comorbid mental and physical health conditions, and educational inequities because they may affect ADHD and interfere with the interpretation of results. Minimization of bias and confounding bias is addressed through well-developed analytic approaches and study design. Representativeness can be improved by engaging underserved communities, addressing participation-related barriers, and using multiple recruitment sites in the population-based studies (Curley, 2024; Enzenbach et al., 2019; Mathur & VanderWeele, 2022). Control for confounders could be enabled by stratification and multivariable regression techniques, whereas measurement accuracy can be enhanced by the use of standardized, culturally sensitive diagnostic tools and corroborated self-reported data (Friis & Sellers, 2021; Khalili et al., 2021). Without bias minimization, findings can be invalid and misleading, resulting in underestimated ADHD prevalence, incidence, and severity in marginalized populations, with the consequent reinforcement of false ideas and assumptions about ADHD in marginalized populations. References Baweja, R., Soutullo, C. A., & Waxmonsky, J. G. (2021). Review of barriers and interventions to promote treatment engagement for pediatric attention deficit hyperactivity disorder care. World Journal of Psychiatry, 11(12), 1206–1227. https://doi.org/10.5498/wjp.v11.i12.1206Links to an external site. Cenat, J. M., Blais-Rochetter, C., Morse, C., Vandette, M.-P., Noorishad, P.-G., Kogan, C., Ndengeyingoma, A., & Labelle, P. R. (2021). Prevalence and risk factors associated with attention-deficit/hyperactivity disorder among US Black individuals. JAMA Psychiatry, 78(1), 21–28. https://doi.org/10.1001/jamapsychiatry.2020.2788Links to an external site. Curley, A. L. C. (2024). Population-based nursing: Concepts and competencies for advanced practice (4th ed.). Springer. Enzenbach, C., Wicklein, B., Wirkner, K., & Loeffler, M. (2019). Evaluating selection bias in a population-based cohort study with low baseline participation: The LIFE-Adult-Study. BMC Medical Research Methodology, 19(1), Article 135. https://doi.org/10.1186/s12874-019-0779-8Links to an external site. Friis, R. H., & Sellers, T. A. (2021). Epidemiology for public health practice (6th ed.). Jones & Bartlett. Khalili, P., Nadimi, A. E., Baradaran, H. R., Janani, L., Rahimi-Movaghar, A., Rajabi, Z., Rahmani, A., Hojati, Z., Khalagi, K., & Motevalian, S. A. (2021). Validity of self-reported substance use: Research setting versus primary health care setting. Substance Abuse Treatment, Prevention, and Policy, 16(1), Article 66. https://doi.org/10.1186/s13011-021-00398-3Links to an external site. Mathur, M. B., & VanderWeele, T. J. (2022). Methods to address confounding and other biases in meta-analyses: Review and recommendations. Annual Review of Public Health, 43, 19–35. https://doi.org/10.1146/annurev-publhealth-051920-114020Links to an external site. McKenna, K., Dona, S. W. A., Gold, L., Dew, A., & Le, H. N. D. (2023). Barriers and enablers of service access and utilization for children and adolescents with attention deficit hyperactivity disorder: A systematic review. Journal of Attention Disorders, 28(3), 259–278. https://doi.org/10.1177/10870547231214002Links to an external site. Instructions: Ask a probing question, substantiated with additional background information, evidence, or research. Share an insight from having read your colleagues’ postings, synthesizing the information to provide new perspectives. Offer and support an alternative perspective using readings from the classroom or from your own research in the Walden Library. Validate an idea with your own experience and additional research. Make a suggestion based on additional evidence drawn from readings or after synthesizing multiple postings. Expand on your colleagues’ postings by providing additional insights or contrasting perspectives based on readings and evidence. Rubric: First Response: (30 points) Post to classmate’s main blog post shows evidence of insight, understanding, or reflective thought about the topic. 30 to >21.0 ptsExcellent• Presents a focused and cohesive viewpoint in addressing this response. • Response includes focused questions or examples related to classmate’s post. • Response stimulates dialogue and commentary. • Posts on separate day. 21 to >12.0 ptsGood• Presents a specific viewpoint that is focused and cohesive. • Response includes at least one focused question or example related to classmate’s post. • There is some attempt to stimulate dialogue and commentary. • Posts on separate day. 12 to >4.0 ptsFair• Presents a specific viewpoint but lacks supporting examples or focused questions related to classmate’s post. • The posting is brief and reflects minimal effort to connect with classmate. • Posts on separate day. 4 to >0 ptsPoor• Response lacks a specific viewpoint and supporting examples or focused questions related to classmate’s post. • The post does not stimulate dialogue or connect with the classmate. • Posts on same day. 30 pts This criterion is linked to a Learning OutcomeSecond Response: (30 points) Post to second classmate’s blog post shows evidence of insight, understanding, or reflective thought about the topic. 30 to >21.0 ptsExcellent• Presents a focused and cohesive viewpoint in addressing this response. • Response includes focused questions or examples related to classmate’s post. • Response stimulates dialogue and commentary. • Posts on separate day. 21 to >12.0 ptsGood• Presents a specific viewpoint that is focused and cohesive. • Response includes at least one focused question or example related to classmate’s post. • There is some attempt to stimulate dialogue and commentary. • Posts on separate day. 12 to >4.0 ptsFair• Presents a specific viewpoint but lacks supporting examples or focused questions related to classmate’s post. • The posting is brief and reflects minimal effort to connect with classmate. • Posts on separate day. 4 to >0 ptsPoor• Response lacks a specific viewpoint and supporting examples or focused questions related to classmate’s post. • The does not stimulate dialogue or connect with the classmate. • Posts on same day. 30 pts
READ MORE >>