Student Name Chamberlain University NR-716: Analytic Methods Prof. Name Date Reflection on Learning and Practice Readiness Introduction The Doctor of Nursing Practice (DNP) program at Chamberlain College of Nursing has provided transformative learning opportunities supported by faculty, staff, and peers. The program emphasizes preparing graduates to generate meaningful and sustainable outcomes in clinical practice using the highest level of nursing expertise (Giardino & Hickey, 2020). Reaching the final week of this course is both an achievement and an opportunity to reflect on the knowledge gained, personal growth, and readiness to transition into the role of a DNP-prepared nurse. Reflection plays a crucial role in self-awareness, professional development, and goal setting. Koshy et al. (2017) highlighted reflection as a key driver of professional progression, fostering the ability to identify knowledge gaps and improve critical thinking. This reflection will address: Analysis and Evaluation of Thinking in Translation Science Challenges in Applying Translation Science Learning about translation science involved exploring different theories and models. This experience highlighted the importance of reading critically, applying logical reasoning, and selecting models that align with clinical problems such as Alzheimer’s disease or hepatocellular carcinoma (HCC). Both conditions pose significant healthcare burdens, and translation models like the Knowledge-to-Action (K-T-A) framework provided structured approaches to applying evidence in practice. However, summarizing translation science concepts posed difficulties. The fear of excluding critical information made it challenging to condense lessons effectively. The process of synthesis formation was particularly demanding, requiring the integration of findings from multiple studies into cohesive, evidence-based recommendations. Despite these challenges, I developed more confidence in synthesizing high-quality research, which is essential in connecting evidence to practical solutions (Mick, 2017). Reflection Through Feedback and Collaboration Constructive feedback from faculty, including Dr. Malate and Dr. Gannon, and discussions with classmates played a critical role in building my confidence. This collaborative environment encouraged deeper reading, careful analysis, and more concise synthesis of evidence. The process also emphasized the importance of stakeholder engagement in translation science. Developing organizational presentations of evidence translation steps helped me appreciate the role of collaboration, leadership, and clear communication in implementing practice changes. Table 1: Challenges and Lessons Learned in Translation Science Area Challenges Faced Lessons Learned/Outcome Understanding theories/models Difficulty in evaluating and selecting appropriate models for practice problems Learned the importance of aligning models (e.g., K-T-A) with specific clinical issues Summarization Fear of omitting vital information when condensing complex concepts Developed skills in extracting and organizing key points without losing meaning Evidence synthesis Struggled to integrate multiple findings cohesively Gained confidence in forming synthesized evidence applicable to practice problems Faculty feedback Initial difficulty applying feedback effectively Learned to use feedback as a constructive tool for growth Stakeholder engagement Lack of clarity on roles in translation process Gained understanding of how collaboration strengthens practice change projects Solution development Uncertainty in applying evidence-based interventions Enhanced motivation and skill in creating practice-based solutions for national health issues Creation of Solutions and the PICOT Question Developing the PICOT question helped simplify my approach, shifting my perspective from overthinking to a clear, structured strategy. This framework guided the search for relevant research articles and interventions that could inform solutions to national practice problems, including Alzheimer’s disease and HCC. Evidence-based practice relies on well-defined PICOT questions, which serve as the foundation for research translation and project development. Preparation for the Role of a DNP-Prepared Nurse Evidence-Based Practice and Clinical Change The prevalence of chronic illnesses such as Alzheimer’s disease highlights the need for evidence-based interventions that improve patient outcomes and reduce healthcare burdens. Synthesizing and translating research into practice helps DNP-prepared nurses lead clinical change and improve quality of care (Cass, 2017; Foloppe et al., 2018; Gronek et al., 2019). The ability to critically appraise research studies and apply them in practice strengthens my readiness to implement practice change projects. Tools such as the John Hopkins Individual Evidence Summary Tool enabled me to evaluate reliability, validity, and applicability of research to clinical settings. Table 2: Preparation as a DNP-Prepared Nurse Competency Application in Practice Critical appraisal of evidence Use structured tools to assess quality, applicability, and validity of research articles Translation of evidence Apply synthesized findings to create sustainable interventions for chronic conditions Leadership in practice change Guide stakeholders and team members in adopting evidence-based practices Application of statistical analysis Interpret descriptive statistical reports to support data-driven decisions Policy and advocacy role Influence healthcare policy by demonstrating the impact of evidence-based interventions Statistical Analysis and Rational Thinking One of the most challenging aspects was mastering descriptive statistics and their application in nursing research. Although numerical interpretation was difficult, I learned that rational thinking and fidelity to study integrity are essential. A DNP-prepared nurse must not alter study findings but instead apply careful analysis and trust in evidence-based processes (Nowell et al., 2017). Conclusion Reflecting on this course reinforces the vital role of translation science in advancing nursing practice. Applying theories, synthesizing evidence, and developing interventions are central to achieving sustainable practice changes. As future nurse leaders, DNP-prepared nurses must embrace reflection, collaboration, and evidence translation to influence both patient outcomes and healthcare policy. The support from faculty and peers has been instrumental in strengthening my readiness to assume advanced practice responsibilities. This reflection not only highlights my growth but also underscores the transformative power of DNP education in shaping leaders equipped to address complex healthcare challenges. References Cass, S. (2017). Alzheimer’s disease and exercise: A literature review. Current Sports Medicine Report, 16(1), 19–22. https://doi.org/10.1249/JSR.0000000000000332 NR 716 Week 8 Discussion: Reflection on Learning and Practice Foloppe, D., Richard, P., Yamaguchi, T., Etcharry-Bouyx, F., & Allain, P. (2018). The potential of virtual reality-based training to enhance the functional autonomy of Alzheimer’s disease patients in cooking activities: A single case study. Neuropsychological Rehabilitation, 28(5), 709–733. https://doi.org/10.1080/09602011.2015.1094394 Giardino, E., & Hickey, J. (2020). Doctor of nursing practice students’ perceptions of professional change through the DNP program. Journal of Professional Nursing, 36(6), 595–603. https://doi.org/10.1016/j.profnurs.2020.08.012 Gronek, P., Balko, S., Gronek, J., Zajac, A., Maszczyk, A., Celka, R., Doberska, A., Czarny, W., Podstawski, R., Clark, C., & Yu, F. (2019). Physical activity and Alzheimer’s disease: A narrative review. Aging and Disease, 10(6), 1282–1292. https://doi.org/10.14336/AD.2019.0226

Student Name Chamberlain University NR-716: Analytic Methods Prof. Name Date Quiz Purpose The primary objective of this quiz is to evaluate your understanding and application of parametric and nonparametric statistical methods. By completing this quiz, you will demonstrate your ability to analyze, interpret, and apply these statistical approaches in research and professional practice. Instructions Course Outcomes This assessment supports achievement of the following course outcome: Due Date All quiz submissions must be completed by Sunday at 11:59 p.m. MT. Quiz Questions and Answers Below is a reformatted table of quiz questions and options, along with answers where applicable: Question Options Correct Answer / Explanation Q1. When testing chemotherapy effectiveness, which is a Type I error? a) Reporting chemotherapy effective when it is not b) Reporting chemotherapy not effective when it is c) Reporting results inconclusive d) No way to determine a) Reporting chemotherapy effective when it is not Type I error = false positive (rejecting null when it’s true). Q2. What is an assumption of the Mann Whitney U test? a) Sample size ≥ 200 b) Scatter “flowerpot” curve c) Independence of observations d) Data are paired c) Independence of observations Q3. With ratio data, true IV, and 2 groups, which test is most likely used? a) John’s Relational Test b) Bonjovi’s Test c) Regression Analysis d) Spearman’s Rank Correlation c) Regression Analysis Q4. A histogram measuring income vs education is plotted. What is the distribution? a) Normal b) Bimodal c) Left Skewed d) Right Skewed Depends on graph → If long tail on right = Right Skewed Q5. Which is least useful in understanding data variability? a) Standard deviation b) Variance c) Mean d) Range c) Mean Q6. What are the four levels of measurement? a) Nominal, Parametric, Linear, Dichotomous b) Parametric, Continuous, Skewed, Interval c) Nominal, Ordinal, Interval, Ratio d) Levels 1–4 c) Nominal, Ordinal, Interval, Ratio Q7. Examples of descriptive statistics are: a) Mean & Mode b) Pearson’s r & Spearman’s r c) t-test & Bonferroni’s test d) Normal & Bimodal distributions a) Mean & Mode Q8. The mean is influenced by: a) Order of data b) Total repeated values c) Individual scores d) Standard deviation c) Individual scores Q9. Type II error occurs when… a) Reject null & reject alternative b) Accept alternative & reject null c) Accept null & reject alternative d) None correct c) Accept null & reject alternative (false negative). Q10. Mean of 6,4,1,9,3,8,3,5,10? a) 5.4 b) 3 c) 6 d) 5 a) 5.4 Q11. Assumptions for Pearson’s r include all EXCEPT: a) Data parametric b) Normal distribution c) Categorical data d) Linear relationship c) Categorical data Q12. Which is continuous? a) Genders b) Class size c) Hair color d) Specialty b) Class size Q13. Type I error occurs when researcher… a) Rejects null & accepts alternative b) Rejects alternative & accepts null c) Accepts null & accepts alternative d) None a) Rejects null & accepts alternative (false positive). Q14. Assumption of Spearman’s Rank Correlation: a) Data parametric b) Normally distributed c) Data at least ordinal d) Categorical/nominal c) Data at least ordinal Q15. Variance tells us: a) Distance of each data point from mean b) Frequency of values c) Average deviation squared d) Deviation from hypothesis c) Average deviation squared Q16. In chemotherapy testing, which error is more dangerous? a) Type I b) Type II b) Type II (risk of ignoring effective treatment). Q17. Assumption for Chi-square test? a) Blinded collection b) Expected values ≥ 5 c) Samples paired d) Sample ≥ 200 b) Expected values ≥ 5 Q18. Frequency distribution informs us about: a) Subgroups into quartiles b) How often each value occurs c) General population d) Significance level b) How often each value occurs Q19. Assumptions for t-test include all EXCEPT: a) Parametric data b) Normal distribution c) Categorical (nominal) data d) Equality of variances c) Categorical (nominal) data Q20. With categorical pre–post data, which test is used? a) Chi-square b) Bonjovi’s Test c) Milli’s Test d) Pearson’s r a) Chi-square Q21. Definition of normal distribution curve? a) Bell-shaped curve, symmetrical around mean Correct: Data are symmetrically distributed in bell curve around mean. Q22. Data unconnected, distinct, nominal/ordinal (e.g., race, marital status): a) Monodicot data b) Discrete data c) Measured data d) Random sample data b) Discrete data Q23. Chemotherapy testing—Type II error consequence? a) Report effective when not b) Report not effective when it is c) Report inconclusive d) No answer b) Report not effective when it is Q24. Four groups, nominal/ordinal data, difference in means → which test? a) Regression b) Pearson’s r c) Kruskal-Wallis d) Spearman’s r c) Kruskal-Wallis Q25. Histogram income vs education, distribution? a) Normal b) Bimodal c) Left Skewed d) Right Skewed Depends on shape; if two peaks = Bimodal. References Grove, S. K., Gray, J., & Burns, N. (2019). Understanding nursing research: Building an evidence-based practice (7th ed.). Elsevier. Polit, D. F., & Beck, C. T. (2021). Nursing research: Generating and assessing evidence for nursing practice (11th ed.). Wolters Kluwer. NR 716 Week 7 Quiz Statistics Creswell, J. W., & Creswell, J. D. (2018). Research design: Qualitative, quantitative, and mixed methods approaches (5th ed.). Sage Publications.

Student Name Chamberlain University NR-716: Analytic Methods Prof. Name Date Using Non-Parametric Statistical Tests Discussion Purpose The main purpose of this discussion is to strengthen the understanding of non-parametric statistical tests and their relevance in clinical research. Non-parametric tests are particularly valuable when dealing with small samples, skewed data, or non-normal distributions, which are commonly observed in healthcare research. By evaluating their use, scholars can determine whether evidence derived from such studies is reliable enough to guide practice change. Instructions As a practice scholar, it is essential to critically evaluate evidence before translating it into clinical practice. In this scenario, a quasi-experimental study is identified as potential support for a practice change. The study attempts to establish a prediction related to correlations between variables. However, the sample size is small, and the data are not normally distributed. This raises questions about whether the statistical test applied—Pearson’s r correlation—is appropriate for such a dataset. To address this scenario, the following guiding questions are explored: Question 1: In your appraisal of the evidence, you note that a Pearson’s r correlation is used to analyze data. Is this the correct level of correlational analysis? Explain your rationale. Pearson’s r correlation is a parametric test that assumes normal distribution, linearity, and equal variance. Since the study sample is small and not normally distributed, Pearson’s r may not be the most appropriate choice. A non-parametric alternative, such as Spearman’s rank-order correlation, would be more suitable. Spearman’s rho evaluates the monotonic relationship between variables without requiring normality. NR 716 Week 6 Using Non-Parametric Statistical Tests Using Pearson’s r in this scenario could lead to biased or inaccurate results because the test’s assumptions are violated. Thus, employing a non-parametric test would yield more credible findings and strengthen the validity of the research outcomes. Question 2: Are association and correlational analysis equivalent in determining relationships between variables? Although association and correlation are related concepts, they are not identical. Concept Definition Key Features Association A broad term indicating that two variables are related in some way. Does not specify the strength, type, or direction of the relationship. Correlation A statistical measure that quantifies the degree and direction of a linear or monotonic relationship. Provides both magnitude (strength) and direction (positive or negative). While both terms describe relationships, correlation is a more specific and quantifiable measure of association. Association can be observed without statistical testing, whereas correlation requires statistical analysis to quantify the strength and direction of the relationship. Question 3: Do these findings impact your decision about whether to use this evidence to inform practice change? Why or why not? Yes, the findings significantly impact the decision to adopt the evidence for practice change. If an inappropriate test such as Pearson’s r is applied to non-normal, small-sample data, the validity of the results becomes questionable. Evidence-based practice requires confidence in the robustness of research findings. In this case, the misuse of statistical tests suggests that the study’s outcomes may not be reliable. Therefore, before integrating this evidence into practice, the study should be reassessed, ideally reanalyzed using non-parametric methods such as Spearman’s rho or Kendall’s tau. Only then can the findings be considered trustworthy for informing clinical decision-making. Program Competencies This discussion supports the development of the following competencies: Course Outcomes Through this discussion, students achieve the following outcomes: References Polit, D. F., & Beck, C. T. (2021). Nursing research: Generating and assessing evidence for nursing practice (11th ed.). Wolters Kluwer. Schober, P., Boer, C., & Schwarte, L. A. (2018). Correlation coefficients: Appropriate use and interpretation. Anesthesia & Analgesia, 126(5), 1763–1768. https://doi.org/10.1213/ANE.0000000000002864 NR 716 Week 6 Using Non-Parametric Statistical Tests Conover, W. J. (1999). Practical nonparametric statistics (3rd ed.). Wiley.

Student Name Chamberlain University NR-716: Analytic Methods Prof. Name Date 1. Perform the following calculations a. Percentage of Patients with Uncontrolled Diabetes Based on the dataset provided, the percentage of patients with uncontrolled diabetes (HbA1c > 7) was determined both pre- and post-implementation of the intervention. Prior to implementation, 9 out of 10 patients (90%) had uncontrolled diabetes. Following the intervention, only 5 out of 10 patients (50%) remained in the uncontrolled category. This indicates a significant improvement in glycemic control after the evidence-based change. b. Mean HbA1c Values The mean HbA1c levels were calculated for both groups of patients. The pre-implementation mean HbA1c level was 7.96, while the post-implementation mean reduced to 7.5. This reduction demonstrates that the intervention had a positive effect on average glucose management. c. Median HbA1c Values The median values provide another measure of central tendency. In the pre-implementation phase, the median HbA1c was 7.65, while in the post-implementation phase, it decreased to 7.0. This decline again supports the effectiveness of the intervention. d. Standard Deviation of HbA1c Levels The standard deviation (SD) was used to measure the spread of HbA1c values. The pre-implementation SD was 1.33, and the post-implementation SD was 1.36. Although the values are relatively close, the slight increase post-implementation suggests that while average levels improved, individual variations remained. e. Range of HbA1c Values The range shows the difference between the highest and lowest HbA1c values. Pre-intervention, the range was 5.0 (11.8 – 6.8), while post-intervention, it slightly decreased to 4.9 (11.3 – 6.4). This indicates relatively consistent control, with outliers still influencing the distribution. Table 1 Descriptive Statistics of HbA1c Levels Pre- and Post-Implementation Measure Pre-Implementation Post-Implementation % of Patients with HbA1c > 7 90% 50% Mean HbA1c 7.96 7.50 Median HbA1c 7.65 7.00 Standard Deviation (SD) 1.33 1.36 Range 5.0 4.9 2. Based on your analysis of the descriptive statistics, what determinations related to the mean HbA1c levels following implementation of the evidence-based intervention can be made? From the descriptive statistics, it is clear that HbA1c levels improved after implementation of the intervention. The mean HbA1c decreased from 7.96 to 7.5, indicating better glycemic control. However, the presence of outliers, particularly patient #10, impacted the distribution of values. This patient consistently had higher HbA1c levels (11.8 pre-intervention and 11.3 post-intervention). These findings highlight the importance of including a larger sample size in future studies. With only 10 patients, the results may not represent the entire patient population. Furthermore, assumptions regarding patient adherence to diet, exercise, and blood glucose monitoring may not hold true for all individuals. For stronger conclusions, the project should involve a more diverse and larger patient population while incorporating follow-up reviews to ensure compliance with lifestyle changes. 3. As you reflect upon HbA1c levels, you observe that patient #10 HbA1c levels are an outlier. What does this do to your understanding of the data? Patient #10’s results clearly represent an outlier, as this individual did not show significant improvement despite the intervention. This affects the overall interpretation of the dataset by pulling the mean higher than it might otherwise be. Outliers such as this can mask the true effectiveness of an intervention for the majority of participants. For patient #10, additional barriers may explain the lack of improvement, including financial constraints, limited social support, advanced age, or challenges in following diet and exercise recommendations. Such cases highlight the importance of tailoring evidence-based interventions to individual patient needs. Advanced practice nurses must recognize potential barriers and incorporate personalized education and support, particularly for patients who require more intensive management. Using descriptive statistics in this way allows clinicians to identify anomalies, guide further assessment, and strengthen interventions for improving patient outcomes (Munoz-Lopez et al., 2020). This reinforces the idea that data interpretation must go beyond averages and consider the context of individual patient results. References Chakrabarty, D. (2021). Measuremental data: Seven measures of central tendency. International Journal of Electronics, 8(1). NR 716 Week 5 Discussion: Analyzing Descriptive Statistics Muñoz-López, D. B., Reyes, V. P., Garay-Sevilla, E. M., & Preciado-Puga, M. D. (2020). Validation of an instrument to measure adherence to type 2 diabetes management. International Journal of Clinical Pharmacy, 43(3), 595–603.

Student Name Chamberlain University NR-716: Analytic Methods Prof. Name Date Evidence Synthesis: Heart Failure Heart failure (HF) is a chronic and complex medical condition where the heart muscle cannot pump sufficient blood to meet the body’s demands. This inefficiency in cardiac output makes treatment management challenging (Piña et al., 2021; Centers for Disease Control and Prevention [CDC], 2019). Congestive heart failure (CHF) often arises from modifiable risk factors such as smoking, diets high in sodium, fat, and cholesterol, sedentary lifestyles, and excessive alcohol intake (CDC, 2023; Mohammadi et al., 2021; Pereira Sousa et al., 2021; Ding et al., 2020). Additionally, comorbidities such as diabetes, obesity, coronary artery disease, and hypertension significantly increase susceptibility to heart failure (CDC, 2023; Mohammadi et al., 2021). Preventive strategies, including early diagnosis and lifestyle modifications, are essential for reducing morbidity and mortality associated with HF. Timely detection of cardiovascular disease allows for prompt treatment initiation (World Health Organization [WHO], 2021). Moreover, ongoing social and clinical support plays a critical role in lowering patient anxiety, improving adherence to treatment plans, and maintaining overall quality of life (Singh et al., 2021; Mohammadi et al., 2021). This synthesis explores the national and state-level impact of HF, evaluates research and non-research evidence, and highlights the benefits of provider support through multimedia education, patient teach-back strategies, and reflective learning. These interventions aim to reduce cardiac anxiety, prevent hospital readmissions, and enhance patient self-care management. Analysis CHF is recognized as one of the leading contributors to global disease burden and mortality. Managing this condition requires patients to have strong knowledge of dietary restrictions, symptom recognition, medication adherence, and lifestyle adjustments such as exercise and daily weight monitoring (Mohammadi et al., 2021; Ding et al., 2020). While the heart still pumps, the decreased circulation of blood and oxygen compromises vital organ function, leading to frequent hospital readmissions when patients fail to self-manage effectively. Patient education is therefore a cornerstone of HF management, reducing readmissions and empowering individuals to take an active role in their care (Pereira Sousa et al., 2021). Epidemiological data reveal alarming prevalence rates. Nearly 6.7 million Americans aged 20 and older are diagnosed with HF, a figure expected to rise to 8.5 million by 2030 (CDC, 2023). Globally, incidence rates range between 1–9 per 1,000 persons annually, with higher risk among individuals aged 55 years and above (Groenewegen et al., 2020). Racial disparities also persist. In 2020, African Americans (non-Hispanic) had the highest mortality rate at 675.4 per 100,000, while Hispanics had a significantly lower rate at 154.8 per 100,000 at the state level (CDC, 2020). At the national level, African Americans recorded 571.8 per 100,000, compared to 315.5 per 100,000 in Hispanics (Mujib et al., 2011). Geographically, southern states such as Alabama and Mississippi report the highest mortality rates linked to HF (CDC, 2020). The financial implications are also significant. Annual healthcare costs for managing HF in the United States average nearly $30,000 per patient (Heidenreich et al., 2022). Without effective interventions, these expenses will likely escalate due to recurrent readmissions and prolonged care. Sustainable strategies that emphasize patient education, long-term follow-up, and support systems can reduce both economic and health burdens. Evidence Synthesis The review of three quantitative research studies highlighted consistent challenges in HF management—particularly poor self-care and insufficient provider support (Mohammadi et al., 2021; Ding et al., 2020). Across the studies, interventions involving daily monitoring, fluid restriction, technology-assisted education, and patient teach-back demonstrated positive outcomes. Patients who engaged in reflective learning showed improved comprehension of disease management and enhanced adherence to treatment regimens. Comparative Summary of the Three Studies Study Design Key Intervention Follow-Up Period Key Findings Mohammadi et al. (2021) RCT Multimedia education + teach-back 8 weeks Improved QoL, reduced anxiety, fewer readmissions Pereira Sousa et al. (2021) RCT Symptom recognition & self-care education 10 weeks Increased self-care awareness and adherence Ding et al. (2020) RCT Telemonitoring + enhanced care program 12 weeks Improved compliance, reduced hospitalizations Despite differences in design, all studies confirmed the effectiveness of continuous provider support, technology-driven education, and patient reflection techniques. Collectively, the evidence underscores the need for structured interventions that combine patient engagement with follow-up care. Practice Question In patients aged 18–60 with heart failure (P), how does multimedia education with teach-back and reflective learning (I), compared to multimedia education only (C), reduce patient anxiety, hospital readmission, and improve quality of life (O) within 8–10 weeks (T)? Evidence suggests that structured discharge education supplemented with follow-up significantly reduces hospital readmissions (Rahmani et al., 2020). Teach-back combined with reflective learning ensures patients internalize medical information, enhancing self-management and quality of life. Translation Science Theory The Knowledge-to-Action (KTA) framework serves as the theoretical model for this practice change. The framework bridges the gap between evidence-based knowledge and practical implementation (Lee & Ho, 2019). Interprofessional collaboration—including physicians, nurses, dieticians, case managers, and patients—ensures holistic care and supports long-term sustainability (Quanbeck, 2019). Conclusion Heart failure remains one of the most complex and costly chronic diseases worldwide. The lack of patient knowledge and self-management skills contributes significantly to high readmission rates and mortality. Evidence supports the integration of multimedia education, teach-back methods, and reflective learning as effective strategies for enhancing patient engagement, reducing anxiety, and improving outcomes. By applying the KTA framework, healthcare systems can bridge the gap between evidence and practice, ensuring that HF interventions are sustainable and impactful. Long-term commitment, stakeholder collaboration, and patient-centered education are essential to reducing the burden of HF and improving quality of life. References Centers for Disease Control and Prevention. (2019). Hospitalization for congestive heart failure, United States. https://www.cdc.gov/nchs/products/databriefs/db108.htm Centers for Disease Control and Prevention. (2020). Interactive atlas of heart disease and stroke. https://nccd.cdc.gov/DHDSPAtlas/reports.aspx Centers for Disease Control and Prevention. (2022). Public health action plan to prevent heart disease and stroke. https://www.cdc.gov/dhdsp/action_plan/pdfs/action_plan_full.pdf Centers for Disease Control and Prevention. (2023). Facts of heart failure in the United States. https://www.cdc.gov/heartdisease/heart_failure.htm Ding, H., Jayasena, R., Chen, S., Maiorana, A., Dowling, A., Layland, J., Good, N., Karunanithi, M., & Edwards, I. (2020). The effects of telemonitoring on patient compliance with self-management recommendations and outcomes of the innovative telemonitoring enhanced care program for chronic heart

Student Name Chamberlain University NR-716: Analytic Methods Prof. Name Date NR 716 Week 3 Discussion Thread Practice Question Hi Dr. Jennings, My practice question is: In families with children, does implementing the Healthy Home Offerings via the Mealtime Environment (HOME) Plus Program, compared to standard mealtime practices, impact nutrition-related outcomes and behaviors over the course of the next 8–10 weeks? (Fulkerson et al., 2018). This question addresses the pressing concern of childhood obesity, which continues to be recognized as a major public health issue with both health and economic implications (Centers for Disease Control and Prevention [CDC], 2022). Because of the long-term risks associated with obesity in children, such as increased likelihood of chronic illnesses in adulthood, it is crucial to establish effective strategies that promote sustainable healthy lifestyles within families. Importance of Collaborative Efforts Addressing the childhood obesity epidemic requires a collective approach. Working together as healthcare providers, researchers, and families can help identify gaps in knowledge and practice while encouraging sustainable change. Leach and Tucker (2018) highlight that bridging the research-practice gap is essential to ensure that evidence-based interventions are effectively applied in real-world settings. Collaborative teamwork allows for shared responsibility and improved outcomes, particularly in creating healthier home environments. Evidence-Based Interventions from the HOME Plus Program The HOME Plus Program provided a structured, family-centered intervention aimed at enhancing the food environment and mealtime practices. The program emphasized: The study enrolled 160 children aged 8–12 years and their parents. Of these, 81 parents were randomized to the intervention group and 79 to the control group. The intervention targeted both parents and children, reinforcing that mealtime routines and role modeling play a significant role in shaping children’s dietary behaviors. NR 716 Week 3 The Practice Question Outcomes of the Study The findings highlighted both parent- and child-centered improvements. The results are summarized below: Outcome Population Results Significance Self-efficacy in identifying portion sizes Parents Intervention group showed significant improvement compared to control Statistically significant (Post-intervention P = .002; Follow-up P = .01) Daily sugar-sweetened beverage intake Children Intervention children consumed fewer sugar-sweetened beverages Statistically significant (P = .04) Long-term impact Parents & Children Positive effects maintained at 21-month follow-up Retention rate of 89% These findings suggest that structured interventions, such as the HOME Plus Program, can improve both short- and long-term nutrition-related outcomes. Importantly, improvements in parents’ ability to recognize healthy portion sizes also positively influenced children’s consumption behaviors. Data Collection and Retention Rates Data were collected using surveys and dietary recalls: High retention rates reinforce the feasibility and acceptability of the intervention in real-world settings, increasing its potential for broader implementation. Implications for Nursing Practice Nurses play a vital role in health promotion and disease prevention. By applying evidence-based programs such as HOME Plus, nurses can support families in adopting healthier routines that improve dietary habits and reduce obesity risk. This aligns with the nursing responsibility of patient education, health coaching, and community-based intervention delivery. Furthermore, the program demonstrates how interdisciplinary collaboration can bridge the gap between research evidence and clinical practice, ensuring that interventions are not only effective in theory but also practical and sustainable in family settings. References Centers for Disease Control and Prevention. (2022). Obesity. https://www.cdc.gov/obesity/strategies/index.html Fulkerson, J. A., Friend, S., Horning, M., Flattum, C., Draxten, M., Neumark-Sztainer, D., Gurvich, O., Garwick, A., Story, M., & Kubik, M. Y. (2018). Family home food environment and nutrition-related parent and child personal and behavioral outcomes of the Healthy Home Offerings via the Mealtime Environment (HOME) Plus Program: A randomized controlled trial. Journal of the Academy of Nutrition and Dietetics, 118(2), 240–251. https://doi.org/10.1016/j.jand.2017.04.006 NR 716 Week 3 The Practice Question Leach, M. J., & Tucker, B. (2018). Current understandings of the research-practice gap in nursing: A mixed-methods study. Collegian, 25(2), 171–179. https://doi.org/10.1016/j.colegn.2017.04.008

Student Name Chamberlain University NR-716: Analytic Methods Prof. Name Date

Student Name Chamberlain University NR-716: Analytic Methods Prof. Name Date Appendix A Johns Hopkins Individual Evidence Summary Tool© The Johns Hopkins Hospital/The Johns Hopkins University EBP Project Practice Question Assessment of knowledge and implementation practices of the ventilator-associated pneumonia (VAP) bundle in the intensive care unit of a private hospital Reviewer Information Evidence Summary The study by Abad, Formalejo, and Mantaring (2021) evaluated the awareness and adherence to the ventilator-associated pneumonia (VAP) bundle within intensive care units (ICUs) across a private hospital. The researchers sought to understand how well nurses and infection control practitioners (ICPs) applied evidence-based strategies to reduce VAP incidence. The VAP bundle, as recommended by the Institute of Healthcare Improvement (IHI), included multiple components such as head-of-bed elevation, daily readiness to extubate, oral care, subglottic suctioning, and prophylaxis for deep vein thrombosis (DVT). Although the majority of participants demonstrated awareness of the VAP bundle, overall compliance was modest. Specific gaps were identified in adherence to readiness-to-extubate assessments, while head-of-bed elevation showed the highest compliance. The authors emphasized the importance of ongoing education, training workshops, and incentive-based retention strategies for nurses to sustain consistent implementation of VAP care practices. Evidence Table Author, Date, and Title Type of Evidence Population, Size, and Setting Intervention Findings that Help Answer the EBP Question Measures Used Limitations Evidence Level & Quality Notes to Team Abad, C. L., Formalejo, C. P., & Mantaring, D. M. L. (2021). Assessment of knowledge and implementation practices of the ventilator acquired pneumonia (VAP) bundle in the intensive care unit of a private hospital. Antimicrobial Resistance & Infection Control, 10(1), 161. https://doi.org/10.1186/s13756-021-01027-1 Descriptive study using both qualitative and quantitative data 60 participants (56 ICU nurses and 4 ICPs) across nationwide ICUs VAP bundle consisting of: – Head-of-bed elevation (30°–45°) – Closed suctioning and subglottic drainage – Daily readiness-to-extubate assessments – Oral hygiene – DVT prophylaxis – Awareness of VAP bundle present among staff, but compliance varied. – Knowledge gaps in guideline details persisted. – Highest compliance: head-of-bed elevation. – Lowest compliance: readiness-to-extubate. – Median bundle compliance was 84.6%. – Education and training essential for improving knowledge. – Nurse retention strategies critical to sustain practices. Surveys, group discussions, and direct observation (16 events observed over three non-consecutive days during day shifts) – Non-randomized design. – Differences in ICU settings could influence results. – Possibility of external confounders. – BPA (Best Practice Alert) did not trigger in ~28% of encounters due to system lockouts. Level III, Good quality Reinforce ongoing education and competency training. Retention incentives for nurses recommended to sustain adherence. Key Insights References Abad, C. L., Formalejo, C. P., & Mantaring, D. M. L. (2021). Assessment of knowledge and implementation practices of the ventilator acquired pneumonia (VAP) bundle in the intensive care unit of a private hospital. Antimicrobial Resistance & Infection Control, 10(1), 161. https://doi.org/10.1186/s13756-021-01027-1 NR 716 Week 1 Discussion