NURS 8201 Use of Regression Analysis in Clinical Practice

Sample Answer for NURS 8201 Use of Regression Analysis in Clinical Practice Included After Question

BY DAY 3 OF WEEK 7

Post a brief description of the article that you selected, providing its correct APA citation. Critically analyze the article by addressing the following questions:

  • What are the goals and purposes of the research study that the article describes?
  • How is linear or logistic regression used in the study? What are the results of its use?
  • What other quantitative and statistical methods could be used to address the research issue discussed in the article?
  • What are the strengths and weaknesses of the study?

Then, explain potential remedies to address the weaknesses that you identified for the research article that you selected. Analyze the importance of this study to evidence-based practice, the nursing profession, or society. Be specific and provide examples.

BY DAY 6 OF WEEK 7

Read a selection of your colleagues’ responses and respond to at least two of your colleagues on two different days 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.
  • Suggest an alternative perspective 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.

A Sample Answer For the Assignment: NURS 8201 Use of Regression Analysis in Clinical Practice

Title: NURS 8201 Use of Regression Analysis in Clinical Practice

Regression analysis is one of the statistical models used in estimating the relationship between variables. The researcher has the ability to determine the effect that an independent variable has on the dependent variable (Willis & Riley, 2017). For example, an increase in one or more values on the independent variable would have an effect on the dependent variable. This paper examines regression analysis was used by an author including its weaknesses and strengths.

Article Summary

            The article authored by Hatakeyama et al., (2019) aimed at finding the relationship between quality of clinical practice guideline (CPGs) and overall assessment scores. This study considered the previous studies that had been done and published between 2011 and 2015. These selected studies were subjected through an independent valuation using AGREE II. The author analyzed the results using a regression analysis. For instance, the analysis included the effect that the six domains and 23 items has on the overall assessment. The study collected a total of 206 CPGs and correlated all the domains to the items on the overall assessment to determine the strength of the relationship before taking the regression analysis on the proposed items.

Use of Regression on the Article

            The author decided to subject domain 3, domain 4, domain 5, and domain 6 of the regression analysis. Domain three represented rigor of development, domain four was for clarity of presentation, domain five was for applicability and finally domain 6 was for editorial independence. The analysis was majoring on how these domains influence the overall assessment (Hatakeyama et al., 2019). The analysis showed that all the domains had a significant relationship with the overall assessment. The author also found that four different items on AGREE II, which were item 8, 15, 19 and 22 had an effect on overall assessment. The regression analysis showed that the change in one unit of the items above had a significant change on the overall assessment which in this case acted as the dependent variable (Hatakeyama et al., 2019). Therefore, the improvement of overall assessment dependent on the increase and decrease of the items that acted as independent variables in this case.

NURS 8201 Use of Regression Analysis in Clinical Practice
NURS 8201 Use of Regression Analysis in Clinical Practice

            Other statistical analysis that could have been used in the study is ANOVA analysis because it shows the strength of the relationship between the items selected. Besides, it allows the researcher to determine the effect that each dependent variables have on each other and how the relationship between the dependent variables can influence the study (Fontaine et al., 2019). Use of ANOVA tests in this study could have strengthened and relayed more information on the collection of items that could have a great impact on the overall assessment.

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            The strength of the regression analysis is on the ability of the author to examine more than one dependent variable. According to the study the author was interested in 22 items and their effect on overall assessment. The study is able to report on the influence of 22 items more easily as compared to other methods that could have been complex (Hatakeyama et al., 2019). Despite the strength that regression analysis has on the study, the method also has its weakness it lacks the ability to examine the relationship between the independent variables considered in the study.

Conclusion

            Regression analysis is a powerful tool in assessing the relationship between dependent and independent variables. The author in the selected the study has the ability to evaluate which of the 22 items have a high or low effect on the overall assessment.

References

Fontaine, G., Cossette, S., Maheu-Cadotte, M. A., Deschênes, M. F., Rouleau, G., Lavallée, A., … & Mailhot, T. (2019). Effect of implementation interventions on nurses’ behaviour in clinical practice: a systematic review, meta-analysis and meta-regression protocol. Systematic reviews8(1), 1-10. https://doi.org/10.1186/s13643-019-1227-x

Hatakeyama, Y., Seto, K., Amin, R., Kitazawa, T., Fujita, S., Matsumoto, K., & Hasegawa, T. (2019). The structure of the quality of clinical practice guidelines with the items and overall assessment in AGREE II: a regression analysis. BMC health services research19(1), 1-8. https://doi.org/10.1186/s12913-019-4532-0

Willis, B. H., & Riley, R. D. (2017). Measuring the statistical validity of summary meta‐analysis and meta‐regression results for use in clinical practice. Statistics in medicine36(21), 3283-3301. https://doi.org/10.1002/sim.7372

Yeom, H. E. (2021). Causal beliefs about hypertension and self-care behaviour in Korean patients. Collegian28(1), 48-56. https://doi.org/10.1016/j.colegn.2020.04.007

A Sample Answer For the Assignment: NURS 8201 Use of Regression Analysis in Clinical Practice

Title: NURS 8201 Use of Regression Analysis in Clinical Practice

This article was authored by Yeom (2020) and focuses on hypertension among Korean patients. According to a study, Korean patients with hypertension tend to believe that their high blood pressure is caused by factors such as heredity, stress, and aging, and as a result, they are less likely to engage in self-care behaviors such as monitoring their blood pressure and eating a healthy diet. The study also found that participants who believed that hypertension was due to controllable factors were more likely to engage in self-care behaviors (Yeom, 2021). This suggests that educational interventions which focus on increasing people’s understanding of the controllable causes of hypertension may be effective at encouraging them to adopt healthier lifestyles.

Goals and Purpose of the Research

The purpose of this study was to explore the causal beliefs about hypertension and self-care behavior in Korean patients. A total of 267 participants completed a questionnaire that assessed their causal beliefs about hypertension, as well as their self-care behavior (Yeom, 2021). The results of the study showed that the most common belief about hypertension was that it is caused by stress. The second most common belief was that hypertension is hereditary. Participants who believed that hypertension is caused by stress were more likely to engage in self-care behaviors, such as monitoring their blood pressure and eating healthy foods. The main goal of the research was to determine if there is causal beliefs about hypertension and self-care behavior among Korean patients. The purpose of this discussion is to analyze the selected article and determine the use of regression analysis in clinical practice.

How Linear or Logistic Regression was used in The Study

Linear regression was used in the study to examine the relationship between causal beliefs and self-care behavior. From the linear regression analysis, the study found that, overall, patients with more linear beliefs about the cause of hypertension were more likely to engage in self-care behaviors (Wongsuriyanan et al., 2020). This suggests that educating patients on the causes of hypertension may help encourage them to better manage their condition.

Other Quantitative and Statistical Methods Could Be Used To Address the Research Issue Discussed In the Article

It is possible to test causal beliefs on self-care intention and medication compliance by using ANOVA instead of linear regression. This would involve creating a model that includes both the independent and dependent variables, as well as a measure of the strength of the relationship between them (i.e., the correlation coefficient) (Liang et al., 2020). Doing this would allow you to determine whether there is a significant relationship between the two variables, after accounting for the variance in both.

Strengths and Weaknesses of the Study

One of the strengths of the study is that it was conducted in a real-world setting with a large sample size. However, the study has several weaknesses, including that it did not control for dietary intake or other lifestyle factors that could have influenced self-care behavior. Additionally, the study did not measure blood pressure or biomarkers of hypertension, which would have been useful to confirm the relationship between causal beliefs and self-care behavior (Yeom, 2021). One of the remedies to the above weakness is the formulation of the control study to confirm the correlation between causal beliefs and self-care. Another remedy is to accurately measure and record hypertension among patients that have been selected for the study.

The results of this study can help inform evidence-based practice by providing information on the most important factors that influence self-care behavior among patients with hypertension. This study is important because it provides empirical evidence for the importance of causal beliefs about hypertension and self-care behaviour (Yeom, 2021). For example, the findings of this study can be used to help inform evidence-based practice in the management of hypertension.

Conclusion

The purpose of this study was to explore the causal beliefs about hypertension among Korean patients and to examine the relationships between these causal beliefs and self-care behaviors. Regression analysis was used to determine the relationship between the variable. The results of this study can help inform evidence-based practice by providing information on the most important factors that influence self-care behaviour among patients with hypertension.

References

Liang, J., Bi, G., & Zhan, C. (2020). Multinomial and ordinal Logistic regression analyses with multi-categorical variables using R. Annals of Translational Medicine8(16). https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7475459/

Wongsuriyanan, C., Phattharayuttawat, S., & Ratta-apha, W. (2020). The Prevalence of Type D Personality and Correlations between Medication Self-Efficacy and Self-Care Behavior in Patients with Hypertension. https://doi.org/10.21203/rs.2.20328/v1

Yeom, H. E. (2021). Causal beliefs about hypertension and self-care behaviour in Korean patients. Collegian28(1), 48-56. https://doi.org/10.1016/j.colegn.2020.04.007

A Sample Answer For the Assignment: NURS 8201 Use of Regression Analysis in Clinical Practice

Title: NURS 8201 Use of Regression Analysis in Clinical Practice

Records and standardized terminologies.

Nursing documentation can raise the bar for nursing care because it is an essential source of information on patient’s needs and nursing interventions. Two examples of standardized terminology that should increase the accuracy of nursing documentation are the Omaha System and NANDA International. The introduction of electronic health records with standardized terminology may help nursing staff feel more assisted in providing nursing care, however, this is still up for debate. A cross-sectional survey with 667 Dutch registered nurses and certified nursing assistants was carried out using an electronic health record with the following objectives: to examine the standardized terminology used by nurses in electronic health records; to ascertain the extent to which they consider the use of electronic health records to be supportive; and to ascertain whether the standardized terminology used by nursing staff in electronic health records is associated with their sense of support. According to De Groot et al. (2020), the respondents were employed by hospitals, nursing homes, or mental health facilities.

Logistic or Linear Regression

The dependent variable in multiple linear regression analysis was the experienced support provided by electronic health records, while the independent variables were the use of standardized terminology (0=no, 1=yes) and sociodemographic characteristics (gender, age, educational attainment, healthcare setting). An analysis using multiple linear regression was then carried out to see whether there was a difference between different standardized terminology. The experienced support provided by electronic health records was the dependent variable in this analysis, while certain standardized terminologies and sociodemographic characteristics were the independent factors (Chi et al., 2017).

Statistical and Quantitative Techniques

Descriptive statistics were employed to characterize the characteristics of the respondents and provide an answer to the first and second research questions. The possible relationships between the use of standardized terminology and the respondent’s healthcare environment were also examined using Pearson’s chi-square test. A one-way ANOVA test was used to investigate if there was a difference in the degree to which respondents felt supported by the use of electronic health records between the respondents’ healthcare settings (Coughlan et al., 2007).

Strength and Weaknesses

A non-validated questionnaire was used because there isn’t one for the experiences of support nurses utilizing electronic health records. Nonetheless, questions were developed in consultation with subject-matter experts and based on the relevant literature.

A preliminary test of the questionnaire was conducted by nursing staff to ensure readability. As such, it is predicted that the questionnaire will demonstrate content validity. Notwithstanding these limitations, our work adds some intriguing knowledge to a nursing practice and research area that is still mostly uncharted territory. One of the study’s strengths is that it was the first to examine the experiences of nursing staff members who all worked in the four primary healthcare settings and had direct patient contact. Our study’s focus on the usage of several standardized terminologies rather than just one and its comparison of their applications is another benefit (De Groot et al., 2020).

Possible Remedies

According to a survey, nursing staff members regularly expressed dissatisfaction with the use of their electronic health data. A different survey study with similar findings also showed that the inadequate usage of electronic health records places a great deal of time pressure on registered nurses. However, there is a lack of understanding of the relationship between the ease of use of electronic health records and the time restrictions related to nursing documentation (Tsai et al., 2020).

References

Chi, C.-Y., Wu, H.-H., Huang, C.-H., & Lee, Y.-C. (2017). Using linear regression to identify critical demographic variables affecting patient safety culture from the viewpoints of physicians and nurses.Hospital Practices and Research,2(2), 47-53.https://doi.org/10.15171/hpr.2017.12

Coughlan, M., Cronin, P., & Ryan, F. (2007). Step-by-step guide to critiquing research. part 1: Quantitative research. British Journal of Nursing,16(11), 658-663.https://doi.org/10.12968/bjon.2007.16.11.23681

De Groot, K., De Veer, A. J., Paans, W., & Francke, A. L. (2020). Use of electronic health records and standardized terminologies: A nationwide survey of nursing staff experiences.International Journal of Nursing Studies,104, 103523.https://doi.org/10.1016/j.ijnurstu.2020.103523

Tsai, C., Eghdam, A., Davoody, N., Wright, G., Flowerday, S., & Koch, S. (2020). Effects of electronic health record implementation and barriers to adoption and use: A scoping review and qualitative analysis of the content.Life,10(12),327.https://doi.org/10.3390/life10120327

Article selected

Yeom, H. -E. (2021). Causal beliefs about hypertension and self-care behaviour in Korean patients. Collegian, 28 (1), 48–56. https://doi.org/10.1016/j.colegn.2020.04.007

Article Summary

The study aim of the article selected is to examine the internal structure underlying the causal beliefs about hypertension in Korean patients and their influence on self-care intention and medication compliance. A cross-sectional study of a convenience sample of 145 patients with an average age of 57.7 was conducted to determine how accurately the independent variable can predict the dependent variable. Causal beliefs about hypertension were assessed using a modified Illness Perception Questionnaire-Revised (IPQ-R). The self-care intention was measured with a 10-item self-care intention scale. Medication compliance was assessed with a single item asking how regularly/irregularly/no intake in the previous three months. Patients who take their medication regularly are categorized as “good compliance” while others are “bad compliance” with prescribed medication.

The four factors underlying the causal beliefs were defined as psychological, fate-related, risk, and habitual factors. According to Tanni et al. (2020), “Regression is indicated when one of the variables is an outcome, and the other one is a potential predictor of that outcome, in a cause-and-effect relationship.” In this case, the differences in causal beliefs depending on sociodemographic factors and their relationships with the seven dimensions of illness perceptions about hypertension were evaluated using multiple linear regression and logistic regression analyses alongside T-test, ANOVA, and Pearson’s correlation coefficient. The influence of causal beliefs on self-care intention and medication compliance was examined. The result shows that causal beliefs about the risk factors (smoking, alcohol) and fate-related factors significantly predict the intent to engage in self-care and medication compliance, respectively.

Strength and Weakness of The Study

             The weakness of this study is that it cannot be generalized to others outside Korean populations because of varying sociocultural contexts. The study’s strength is that it highlights the need to pay special attention to those individuals who believe in a supernatural power or engage in risky behavior (smoking, alcohol consumption) to determine the possible gap in causal beliefs and scientific evidence of the cause of hypertension (Yeom, 2021).

Other Applicable Quantitative and Statistical Methods

Regression is a “statistical technique to formulate the model and analyze the relationship between the dependent and independent variables” (Jain & Priya, 2019, paragraph 3). I believe this study is detailed by using T-test, ANOVA, Pearson’s correlation coefficient, multiple linear regression, and logistic regression analyses. I do not think any other applicable quantitative and or statistical methods are applicable for this study.

References

Jain, R., & Priya C. (2019). How to interpret the results of the linear regression test in SPSS? Knowledge Tank; Project Guru. https://www.projectguru.in/interpret-results-linear-regression-test-spss/

Tanni, S. E., Patino, C. M., & Ferreira, J. C. (2020). Correlation vs. regression in association studies. Journal Brasileiro de Pneumologia, 46(1). https://doi.org/10.1590/1806-3713/e20200030

Yeom, H. -E. (2021). Causal beliefs about hypertension and self-care behaviour in Korean patients. Collegian, 28 (1), 48–56. https://doi.org/10.1016/j.colegn.2020.04.007