NUR 590 Evidence-Based Practice Project Proposal Evaluation Plan

NUR 590 Evidence-Based Practice Project Proposal Evaluation Plan

Assessment Description

In 750-1,000 words, develop an evaluation plan to be included in your final evidence-based practice project proposal. You will use the evaluation plan in the Topic 8 assignment, during which you will synthesize the various aspects of your project into a final paper detailing your evidence-based practice project proposal.

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Provide the following criteria in the evaluation, making sure it is comprehensive and concise:

  1. Discuss the expected outcomes for your evidence-based practice project proposal.
  2. Review the various data collection tools associated with your selected research design and select one data collection tool that would be effective for your research design. Explain how this tool is valid, reliable, and applicable.
  3. Select a statistical test for your project and explain why it is best suited for the tool you choose.
  4. Describe what methods you will apply to your data collection tool and how the outcomes will be measured and evaluated based on the tool you selected.
  5. Propose strategies that will be taken if outcomes do not provide positive or expected results.
  6. Describe the plans to maintain, extend, revise, and discontinue a proposed solution after implementation.
  7. Evidence-Based Practice Project Proposal Evaluation Plan NUR 590

Refer to the “Evidence-Based Practice Project Proposal – Assignment Overview” document for an overview of the evidence-based practice project proposal assignments.

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You must cite at least five peer-reviewed sources to complete this assignment. Sources must be published within the last five years and appropriate for the assignment criteria and nursing content.

Complete the “APA Writing Checklist” to ensure that your paper adheres to APA style, formatting criteria, and general guidelines for academic writing. Include the completed checklist as an appendix at the end of your paper.

Prepare this assignment according to the guidelines in the APA Style Guide in the Student Success Center.

This assignment uses a rubric. Please review the rubric prior to beginning the assignment to become familiar with the expectations for successful completion.

You are required to submit this assignment to LopesWrite. A link to the LopesWrite technical support articles is located in Class Resources if you need assistance.

Let’s explore the distinction between statistically significant evidence and clinically significant evidence and how each of these findings can be employed to advance an evidence-based practice project.

Statistically significant evidence and clinically significant evidence, although related, convey distinct meanings. Statistical significance hinges on the concept of the probability value (p-value). It provides researchers with insights into the likelihood or chance that the study’s results are a random occurrence rather than a genuine difference between the variables (Heavey, 2015).

In the realm of statistical significance, the alpha value plays a crucial role. When the analysis of the study’s results is conducted and the p-value is found to be less than the alpha value, it signifies that the data indicates a real result, not one arising from chance, rendering the results statistically significant.

Conversely, clinical significance goes beyond statistical significance to assess whether the statistical findings hold practical importance and can guide patient care (Heavey, 2015). In other words, it evaluates whether the observed statistical significance is substantial enough to warrant changes in clinical practice.

It’s worth noting that a study’s results can be statistically significant but not necessarily clinically significant. This scenario arises when the statistical findings, while valid, do not translate into a meaningful impact on clinical practice and patient care. Determining clinical significance typically follows the establishment of statistical significance (Heavey, 2015).

These distinct findings play pivotal roles in advancing an evidence-based practice (EBP) project. Statistical significance is a crucial first step in EBP as it helps determine whether an observed effect is likely due to chance or if there is a genuine relationship between variables. In essence, it gauges the strength of the evidence against the null hypothesis, providing a foundation for EBP project decisions (Armijo-Olivo, 2018).

Clinical significance, on the other hand, is instrumental in guiding EBP by assessing the practical implications of the findings. It helps answer whether the results of a study are meaningful in a clinical context. In essence, it evaluates if the intervention under investigation has a substantial impact, making it worthwhile to implement changes in clinical practice despite any associated costs, inconveniences, or potential harms (Armijo-Olivo, 2018).

In summary, while statistical significance addresses the likelihood of results occurring by chance, clinical significance delves into the practical implications of these results for patient care. Both forms of significance play vital roles in advancing evidence-based practice by providing a robust foundation for decision-making and ensuring that interventions are not only statistically valid but also clinically meaningful.


Armijo-Olivo, S. (2018). The importance of determining the clinical significance of research results in physical therapy clinical research. Brazilian Journal of Physical Therapy, 22(3), 175-176.

Heavey, E. (2015). Differentiating between statistical significance and clinical significance. American Nurse today, 10(5), 26-28.

Assessment Description

Let’s delve into the distinction between statistically significant evidence and clinically significant evidence, and explore how these findings can be employed to advance evidence-based practice projects.

Statistically significant evidence is a result indicating that an observed occurrence is not a mere product of chance. Statistical significance primarily revolves around the null hypothesis, the p-value (representing probability), and the predetermined significance level based on previously collected data (Ranganathan et al., 2017). In essence, statistics help researchers, organizations, and various entities gauge the magnitude of experimental, survey, or poll results. Statistical significance aims to ensure that a genuine effect is present, making it a valuable tool for decision-makers. However, it’s important to note that statistical significance does not inherently determine the truth, effectiveness, or practical relevance of the findings.

On the other hand, clinically significant evidence pertains to results that are not only observed but also measurably impacted through intervention. Clinical significance considers factors such as effect size, the number needed to treat (reflecting the sample size affected), and the Jacobson-Truax index (assessing reliability change) (MHA, 2021). In practice, clinical findings often hold greater replicability compared to solely statistically significant findings, especially in high-stakes situations where even a slight margin of error is unacceptable. Clinical relevance aims to provide insights into the depth and extent of an effect, serving as a crucial tool for policymakers, particularly in the fields of pharmacology, psychology, and medicine.

In the context of evidence-based research practice, it’s essential to prioritize the assessment of statistical significance before delving into clinical significance. The evidence-based clinical significance of a research project is instrumental in advancing projects by aligning them with statistically significant results. This approach ensures that evidence-based practices are not only grounded in statistical validity but also rooted in their real-world impact, thus enhancing the quality and effectiveness of projects (MHA, 2021).

In summary, while statistical significance addresses the probability of results occurring by chance, clinical significance delves into the tangible and meaningful impact of interventions. Both forms of significance play vital roles in advancing evidence-based practice by providing a comprehensive understanding of the results, from their statistical validity to their practical relevance, ultimately guiding decision-making and project development.

MHA. (2021). Clinical Significance vs. Statistical Significance – Side-by-Side Comparison. Mhaonline.

Ranganathan, P., Pramesh, C., & Buyse, M. (2017). Common pitfalls in statistical analysis: Clinical versus statistical significance. Perspectives in Clinical Research6(3), 169.


Evaluation is a crucial phase in the process of evidence-based practice. Therefore, it’s vital for those implementing change to develop a well-thought-out evaluation plan to guide this phase effectively. Evaluation serves the essential purpose of providing insights into the project’s effectiveness in achieving its set objectives (Hopp & Rittenmeyer, 2021). Furthermore, it aids in assessing the efficacy of the methods employed in attaining the project’s goals. It is of utmost importance to communicate the evaluation plans to all stakeholders in a timely manner to ensure a comprehensive understanding of every aspect of the project. Hence, the primary objective of this assignment is to construct an evaluation plan for the proposed evidence-based practice (EBP) project.

Expected Outcomes:

The project’s focus is on utilizing automated fall detectors to reduce the incidence of falls among elderly patients aged sixty-five and above. Consequently, several anticipated outcomes can be identified for the proposed EBP project. Firstly, a reduction in the number of falls among elderly patients admitted to the facility is expected (Bet et al., 2019). The automated fall detectors are anticipated to assist the nursing staff in detecting potential patient falls, thus preventing them. Additionally, the project will involve training the nursing staff in using these automated fall detectors. Consequently, another expected outcome relates to enhanced knowledge among nurses regarding the utilization of automated fall detectors to prevent patient falls. It is also expected that the organization will incorporate this solution into the standard care for elderly patients admitted to the facility.

Data Collection Tools:

Selecting the appropriate data collection tools is pivotal in ensuring the collection of relevant data, which is essential for evaluating the project’s effectiveness. Therefore, various data collection tools will be employed in this project. Electronic health records will serve as a primary source of data regarding patient fall incidents, both before and after the implementation of the intervention. Another important tool will be questionnaires (Linsley et al., 2019), which will be instrumental in assessing the nurses’ knowledge concerning the use of automated fall detectors to mitigate patient falls.

Statistical Tests:

Statistical tests play a crucial role in presenting the intended project outcomes and interpreting the effectiveness of the interventions. One of the key tests to be employed is the paired sample t-test. Paired sample t-tests have demonstrated their effectiveness in pre- and post-intervention comparisons (Grove & Cipher, 2019). Given that this project aims to explore the rates of patient falls before implementing automated fall detectors and after the intervention, the paired sample t-test is invaluable in determining the disparity between these two phases to evaluate the efficacy of the intervention.

Methods for Data Collection Tool:

Ensuring data completeness is essential. Therefore, two different individuals will be tasked with verifying the questionnaires, with a focus on ensuring that all questions have been answered. Data will also be extracted from both electronic health records and questionnaires for analysis to assess the effectiveness of the intervention. As part of the plan, the extracted data will be analyzed using statistical software. The evaluation process will employ both process and outcome measures. While outcome measures will ascertain whether the anticipated results have been achieved, process measures will focus on determining the efficacy of the applied project intervention.

Strategies in Case of Unexpected Results:

In some instances, the project’s outcomes may not align with the anticipated results. Consequently, one strategy to consider is a reexamination of the project’s intervention. This process can provide valuable insights into potential areas of weakness and strategies to enhance those areas (Melnyk, B. M., & Fineout-Overholt, 2019). The project’s timeline can also be extended. An extension allows for the implementation of corrective measures or interventions, which can then be monitored to improve the project’s effectiveness.

Plans for Maintaining, Extending, Revising, and Discontinuing the Proposed Solution:

The project will be maintained if the outcomes align with expectations and positively impact clinical practice. Additionally, cost-effectiveness will be a criterion for maintaining the project. In case of inconclusive outcomes, an extension will be pursued to gather more data for evaluation (Melnyk, B. M., & Fineout-Overholt, 2019). Project revision will be considered if strategies do not align with the outcomes, providing an opportunity to enhance interventions for greater project success. Lastly, if the project causes harm to patients and staff in the facility, discontinuation will be considered.


Evaluation is a cornerstone of the evidence-based practice project. As such, the formulation of a comprehensive plan is crucial. This discussion has explored various strategies for use in the proposed project, shedding light on how effectiveness in achieving project aims and the implementation of strategies will be assessed. It will provide a reflection of the project’s effectiveness in accomplishing its objectives and how well the implemented strategies have performed.

Evidence-Based Practice Project Proposal Evaluation Plan NUR 590 Reference

Bet, P., Castro, P. C., & Ponti, M. A. (2019). Fall detection and fall risk assessment in older person using wearable sensors: A systematic review. International Journal of Medical Informatics130, 103946.

Grove, S. K., & Cipher, D. J. (2019). Statistics for Nursing Research: A Workbook for EvidenceBased Practice. Elsevier.

Hopp, L., & Rittenmeyer, L. (2021). Introduction to Evidence-Based Practice: A Practical Guide for Nursing. F.A. Davis.

Linsley, P., Kane, R., & Barker, J. H. (2019). Evidence-based Practice for Nurses and Healthcare Professionals. SAGE.

Melnyk, B. M., & Fineout-Overholt, E. (2019). Evidence-based Practice in Nursing & Healthcare: A Guide to Best Practice. Wolters Kluwer.

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