Assignment Task:
Dinorah
A confidence interval (CI) is a range of values, derived from sample data, that is likely to contain the true population parameter, and it plays a critical role in helping Doctor of Nursing Practice (DNP) leaders interpret study results with greater clarity and precision. P-value or statistical significance is not the only way in which evidence-based practice, and in DNP role in particular, can interpret data; interpretation of results and how those results can be applied in practice are the most important ones. This is achieved through a margin of error and the certainty level of point estimates (mean, proportion or odds ratio) which is that: confidence intervals can lead to a more informed clinical decision.
A confidence interval gives more information than a single number confidence statistic or a p-value. For example, if a study finds that a nurse-led intervention reduces hospital readmissions by an average of 8%, a 95% CI of 4% to 12% suggests that the true effect of the intervention is very likely within this range (Hayat et al., 2021). This will result in giving the DNP leader more actionable information than mere appreciation of the fact that the result is of statistical significance. A small CI implies good precision of the estimate, whereas, a large CI means increased variability in data, or uncertainty and therefore should be interpreted with caution. Therefore, DNPs can use CIs to judge the reliability and clinical relevance of research findings before implementing new interventions or protocols in patient care settings (Bradley et al., 2023).
Furthermore, in undergoing quality improvement efforts or assessing the clinical interventions, DNP should not only establish whether an intervention is effective, but rather how minimally or maximally it is effective and under which conditions it can be operationalized. The application of confidence intervals aligns closely with DNP Essential III: Clinical Scholarship and Analytical Methods for Evidence-Based Practice, which emphasizes the use of analytical methods to critically appraise existing literature and apply evidence to improve practice outcomes (Waldrop et al., 2023). This degree of scrutiny becomes possible through the use of confidence intervals. Suppose that two distinct programs of wound care are compared, and it is found that the first one demonstrates the healing rate of 85% with the 95% CI of 82%-88%, whereas the second one exhibits the 80% value of healing with the 95% CI of 70%-90%. In this case, the former protocol will provide better specificity and reliability of the results. Such a greater understanding gives the DNP the authority to recommend the more credible form of intervention when coming up with the clinical guidelines or the standard operating procedures.
Moreover, when confidence interval is taken together with variability, sample size, and confidence levels, it is based on biostatistics and the science of measurement. Based on this, the utilization of confidence intervals in a study supports DNP Essential I: Scientific Underpinnings for Practice, as it calls for advanced practice nurses to integrate scientific knowledge into practice (Waldrop et al., 2023). Through a proper understanding of CIs, DNPs can differentiate between statistically but not clinically significant findings and clinically and statistically essential findings. For example, if a study on a new antihypertensive drug shows a systolic blood pressure reduction of 2 mmHg with a 95% CI of 0.5 to 3.5 mmHg, a DNP might question the clinical value of such a modest reduction, even if the result is statistically significant (Bradley et al., 2023). Such discernment is necessary to avoid the adoption of interventions that offer little real-world benefit or are not worth the effort in terms of cost, burden, or risk. Need Assignment Help?
References:
Bradley, C., Boykin, A., & Kilmer, M. (2023). Enhancing DNP project success: a statistical collaboration approach. Nurse Educator, 48(1), 37-42.
Hayat, M. J., Kim, M., Schwartz, T. A., & Jiroutek, M. R. (2021). A study of statistics knowledge among nurse faculty in schools with research doctorate programs. Nursing Outlook, 69(2), 228-233.
Waldrop, J., Reynolds, S. S., McMillian-Bohler, J. M., Graton, M., & Ledbetter, L. (2023). Evaluation of DNP program essentials of doctoral nursing education: A scoping review. Journal of Professional Nursing, 46, 7-12.