BLOG: CRITIQUING SOURCES OF ERROR IN POPULATION RESEARCH TO ADDRESS GAPS IN NURSING PRACTICE
EPIDEMIOLOGY
BLOG: CRITIQUING SOURCES OF ERROR IN POPULATION RESEARCH TO ADDRESS GAPS IN NURSING PRACTICE
Respond to
two colleagues in one or more of the following ways:
· 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.
PEER # 1
Week 6 discussion: Initial post
Health Issue: Diabetes mellitus (Type 2) is the selected issue for this discussion.
Population: Low-income, minority adolescents
Practice Gap: While DNP prepared nurses play a vital role in managing diabetes in various settings, a gap exists in developing and implementing culturally sensitive interventions to improve diabetes self-management education (DSME) for low-income, minority adolescents. These adolescents often face social determinants of health that exacerbate diabetes control, requiring a tailored approach (American Association of Diabetes Educators, 2023). DNPs can bridge this gap by:
· Implementing telehealth consultations with endocrinologists or diabetes educators for rural patients.
· Developing and leading community-based diabetes education and self-management programs.
· Partnering with local agencies to enhance access to healthy food choices and opportunities for physical activity.
· Advocating for policies that address social determinants of health and improve healthcare infrastructure in rural communities.
Biases in Research
Understanding potential biases is crucial for DNPs to critically appraise research used to inform practice. The following are brief explanations of four common biases:
Selection Bias: This occurs when the sample population studied does not accurately represent the target population, leading to inaccurate generalizability of findings. For example, research on DSME interventions might only recruit adolescents from middle-class backgrounds, neglecting the specific needs of low-income youth (Guyatt et al., 2011]).
Information Bias: This arises when how data is collected or measured influences the results. In DSME research, information bias could occur if self-reported adherence to diabetes management plans is not verified with objective measures like blood sugar monitoring (Guyatt et al., 2011).
Confounding: This happens when a third variable, not accounted for in the study design, influences both the exposure variable (e.g., DSME intervention) and the outcome variable (e.g., glycemic control). For instance, socioeconomic factors like access to healthy food could confound the results of a DSME program, making it difficult to isolate the intervention’s true effect (Centers for Disease Control and Prevention, 2023).
Random Error: This refers to chance variation in the study results unrelated to the intervention itself. Random error can be minimized through robust research designs and large sample sizes (Guyatt et al., 2011).
By recognizing these biases, DNPs can critically evaluate research informing DSME interventions for low-income, minority adolescents and advocate for culturally sensitive approaches that address the specific needs of this population.
Impact of Bias and Confounding Awareness on Treatment
Nurses educated at the doctoral level who have an understanding of bias and confounding elements can critically appraise research used to guide treatment decisions for low-income adults with diabetes. Also, having an awareness of selection bias allows for the identification of generalizability limitations in research findings. DNPs can then seek out studies that include low-income populations to ensure recommendations are applicable. Furthermore, recognizing information bias encourages DNPs to assess the quality of data collection methods used in research and this helps them determine the reliability of study conclusions. Another advantage of understanding confounding allows DNPs to identify and adjust for factors that might skew the results of research on diabetes management in low-income populations leading to the strengthening of the evidence base for treatment decisions. Finaly, by being aware of these potential biases and confounding factors, DNPs can translate research findings into effective and culturally sensitive diabetes management programs for low-income adults.
Strategies to Minimize Bias and Confounding
These include the following:
Study Design: Researchers can employ randomized controlled trials (RCTs) to minimize selection bias and ensure control groups receive alternative interventions (Guyatt et al., 2011). Stratified sampling ensures representation of subgroups within the rural population.
Analysis Considerations: Statistical methods like propensity score matching can adjust for confounding variables, accounting for their influence on the observed relationship between diabetes risk factors and outcomes (Rosenbaum, 2010)
Consequences of Unmitigated Biases
Unrecognized biases can lead to misinterpretations of research, potentially resulting in:
Ineffective Interventions: Programs based on biased research might not address the true needs of the target population, leading to poor outcomes.
Also, uncontrolled bias can lead to wasted resources on ineffective interventions and missed opportunities to improve health outcomes for low-income, minority communities with diabetes. For example, if a biased study suggests that a particular diabetes education program is ineffective, DNPs may not recommend it to their patients, even though it could be beneficial.
Health Disparities: Biased research may exacerbate health disparities if interventions are not tailored to address the unique challenges of different populations.
If selection bias goes unchecked, interventions found to be effective in research may not work as well in real-world settings. Confounding variables can lead to misinterpretations of cause-and-effect relationships, potentially leading to inappropriate or ineffective treatment recommendations for patients with diabetes, particularly in vulnerable populations.
Conclusion
In conclusion by being aware of and minimizing biases, DNPs can ensure research findings are accurately translated into evidence-based practice, improving patient care and health outcomes for all populations.
References
American Association of Diabetes Educators. (2023). Standards of practice for diabetes self-management education/support.
to an external site.
Centers for Disease Control and Prevention. (2023). Social determinants of health inequalities.
to an external site.
Guyatt, G. H., Ospina, T., Cadegiani, G., Payne, M. E., Sullivan, M. J., & Devereaux, R. V. (2011). GRADE: An emerging consensus on rating quality of evidence and strength of recommendations.
BMC Medical Research Methodology, 11, 2.
to an external site.
Guyatt, G. H., Ospina, M. B., Sultan, J. A., Strauss, B. E., Bullock, T. L., & Devereaux, R. V. (2011). GRADE: Going from evidence to recommendations.
Canadian Medical Association journal, 183(14), 1613 – 1619.
to an external site.
Rosenbaum, P. R. (2010). Observational studies.
Springer Series in Statistics (Vol. 208). Springer.
·
PEER # 2
Week 6 Initial Post:
CRITIQUING SOURCES OF ERROR IN POPULATION RESEARCH TO ADDRESS GAPS IN NURSING PRACTICE
Practice Gap: Lack of accessible transportation for patients living in rural communities significantly affects their ability to reach healthcare appointments, creating a critical barrier to timely and effective medical care. This issue is deeply rooted in the unique challenges of rural living, including sparse public transportation networks, the considerable distances to healthcare providers, and the economic strain of travel costs. Consequently, rural patients often face delays in receiving necessary care, which can lead to the deterioration of health conditions and higher healthcare expenses over time.
Treatment of this population/issue could be affected
Recognizing bias and confounding factors in epidemiologic studies about transportation barriers in rural areas can profoundly influence the approach to mitigating these issues, ensuring that interventions are grounded in a realistic context of the patient’s circumstances. It enables healthcare planners and policymakers to devise strategies that accurately address the specific needs and barriers faced by rural communities, rather than relying on skewed data that may overlook critical aspects of their challenges (Syed et al., 2013). This awareness can lead to more equitable healthcare solutions by highlighting the need for tailored interventions that consider the unique socioeconomic and geographical factors affecting rural populations, thus improving the effectiveness of programs aimed at increasing healthcare accessibility.
Strategies researchers can use to minimize these types of bias in studies
To mitigate bias in studies exploring the lack of accessible transportation for patients in rural areas, researchers can adopt a longitudinal study design, tracking healthcare access over time to better understand the dynamic relationship between transportation barriers and healthcare utilization. This method reduces recall bias and provides a more accurate picture of how transportation issues affect healthcare access longitudinally (Varela et al., 2019). Another effective strategy is to use propensity score matching to control for confounding variables, ensuring that the comparison between individuals with and without transportation barriers is as fair and unbiased as possible. This technique adjusts for factors that might skew the relationship between transportation access and healthcare outcomes, enabling researchers to isolate the effect of transportation barriers more accurately.
Effects these biases could have on the interpretation of study results
Failing to address these biases could result in skewed interpretations of research findings, possibly overstating the accessibility of healthcare for rural patients or understating the severity of transportation challenges. Such inaccuracies might misguide healthcare policy and resource allocation, leading to ineffective solutions that do not adequately tackle the pressing needs of those in rural communities. ((Kaiser & Barstow, 2022)
Reference:
Kaiser, N., & Barstow, C. K. (2022, February 14). Rural transportation infrastructure in low- and middle-income countries: A review of impacts, implications, and interventions. MDPI.
to an external site.
Syed, S. T., Gerber, B. S., & Sharp, L. K. (2013). Traveling towards disease: transportation barriers to health care access. Journal of Community Health, 38(5), 976–993.
to an external site.
Varela, C., Young, S., Mkandawire, N. et al. TRANSPORTATION BARRIERS TO ACCESS HEALTH CARE FOR SURGICAL CONDITIONS IN MALAWI a cross-sectional nationwide household survey. BMC Public Health 19, 264 (2019).
to an external site.
Assignment Rubric Details
Close
Rubric
NURS_8310_Week6_Blog_Rubric
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NURS_8310_Week6_Blog_Rubric |
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Criteria |
Ratings |
Pts |
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This criterion is linked to a Learning OutcomeMain Posting: Idea and Content |
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60 pts |
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This criterion is linked to a Learning OutcomeFirst Response: Post to colleague’s main blogpost shows evidence of insight, understanding, or reflective thought about the topic. NOTE: Responses to faculty are not counted as first or second colleague responses. |
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20 pts |
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This criterion is linked to a Learning OutcomeSecond Response: Post to second colleague blog post shows evidence of insight, understanding, or reflective thought about the topic. |
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20 pts |
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Total Points: 100 |
