NURS 8201 t-Tests and ANOVA in Clinical Practice

BY DAY 3 OF WEEK 5

Post a brief description of the topic that you selected for this Discussion. Summarize the study discussed in your selected research article and provide a complete APA citation. Be sure to include a summary of the sample studied, data sources, inferential statistic(s) used, and associated findings. Then, evaluate the purpose and value of this particular research study to the topic. Did using inferential statistics strengthen or weaken the study’s application to evidence-based practice? Why or why not? Be specific and provide examples.

BY DAY 6 OF WEEK 5

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.

WEEK 5 DISCUSSION: T-TESTS AND ANOVA IN CLINICAL PRACTICE

NURS 8201 t-Tests and ANOVA in Clinical Practice

The research chosen for this discussion was conducted to evaluate the intensive care nurse attitudes on evidence-based nursing. Nurse introduction forms and evidence based-questionnaires were used to collect data on 70 nurses in public hospitals. Evaluation of the data r using ANOVA revealed that “ the mean score based on the questionnaires was 57.20±9.06, while the highest score of attitudes was (26,97±5,50) is in the sub-dimension of beliefs and expectations towards evidence-based nursing” (Dikmen et al., 2018). A positive correlation between the data variables using ANOVA analysis suggested that nurses positively affect intensive care. However, a comparison between nurse education and duration in the intensive care unit had no significant difference.

Nurses constitute the largest group of professionals in the healthcare industry. Research on their attitudes towards evidence-based nursing in intensive care is essential to evaluate the best practices and adapt them to their practice (Liyew et al., 2020). Determining the nurse attitudes facilitates developing strategies that raise evidence-based nursing practice in the areas (Dikmen et al., 2018). Additionally, nurses need to keep updated through regular journals of scientific research methods to maintain a positive attitude towards evidence-based nursing.

The inferential statistics in the paper played a significant role in examining the rate of nurses who are positive towards evidence-based nursing practices in the intensive care unit. The use of inferential analysis helps generate a solid explanation of the situation. Conclusions are drawn based on extrapolation using descriptive statistics of the data collected. Therefore the inferential statistics strengthened the study application on evidence-based practices by identifying the relationship between the data variables collected (Hare, 2020). Evaluation of the data collected through the forms and the questionnaires suggests that nurses are positive towards evidence-based nursing in intensive care units.

Reference

Dikmen, Y., Filiz, N. Y., Tanrıkulu, F., Yılmaz, D., & Kuzgun, H. (2018). Attitudes of intensive care nurses towards evidence-based nursing. International Journal of Health Sciences and Research8(1), 138-143.

Hare, K. A. (2020). Evidence-based end-of-life care education for intensive care nurses (Doctoral dissertation, Walden University).

Liyew, B., Dejen Tilahun, A., & Kassew, T. (2020). Knowledge, attitude, and associated factors towards physical assessment among nurses working in intensive care units: a multicenter cross-sectional study. Critical Care Research and Practice2020.https://doi.org/10.1155/2020/9145105

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In a descriptive cross-sectional design survey to identify predictors of Intensive Care Unit (ICU) nurse’s practice of evidence-based practice (EBP) guidelines, the result reviewed two variables associated with why ICU nurses are not consistently practicing EBP (Ashraf et al. 2020). One hundred thirty-two participants were conveniently recruited from the ICU of two different hospitals. Self-reported questionnaires were utilized, including the EBP questionnaire and EBP barrier scale. Pearson correlation test, student t-test, and ANOVA were conducted. The analysis revealed that the mean score of ICU nurses’ EBP was 4.29 (SD = 1.50) out of 7, which was the low level of EBP. This result compared with the prior researcher at 4.3 out of 7. Attitude and knowledge were the significant predictors for the practice of EBP, while the biggest barrier to practice EBP was the setting barrier, with a mean score of 3.02 out of 4. Setting barrier was explained as lack of resources regarding health care, overload work, and low support from hospital administration to apply EBP (Ashraf, 2020).

NURS 8201 t-Tests and ANOVA in Clinical Practice
NURS 8201 t-Tests and ANOVA in Clinical Practice

The application of statistical analysis help researchers reveals a potential issue with why EBP is lacking in ICU. The study’s outcome added a new predictor and gave direction to how educational programs for nurses and improvement in setting barriers can enhance the practice of EBP at the workplace.

Reference

Ashraf A., Omar T., Jamal A. S., Omar, A., & Muhammad W. D. (2020). Predictors of Intensive Care Unit Nurses’ Practice of Evidence-Based Practice Guidelines. Inquiry: The Journal of Health Care Organization, Provision, and Financing57https://doi.org/10.1177/0046958020902323

. To begin with, nurses are evidence-based practice (EBP) advocates and agents of evidence-based change in their clinical setting. I agree with you that nurses are also the largest health care workforce. Therefore, they can have a major impact on the provision of evidence-based care to patients. However, nurses can also face barriers to EBP in their practice setting (Shayan et al., 2019). The study you have chosen for this discussion is equally relevant and informative. On the one hand, it provides data to describe nurses’ attitudes toward EBP (Dikmen et al., 2019). On the other hand, it illustrates how descriptive and inferential statistics can be helpful in the analysis of correlations. Yet, how consistent is the statistical analysis performed in the study?

           As mentioned in your post, the researchers in your chosen study used both descriptive and inferential statistics. For the latter, Dikmen et al. (2019) used ANOVA, independent t-test, and Pearson correlation. Each of these have proved useful in the analysis of statistical correlations. However, it appears that researchers often fail to include essential information about the statistical tests used, thus limiting the transparency of their research procedures. Weissgerber et al. (2018) say that researchers should not only specify the type of ANOVA used (i.e., one- or two-way ANOVA) but also include the name and level for each factor, report the F-statistic, and mention if any post-hoc tests were performed.

The study mentioned in your post includes just a brief discussion of both the methods and results (Dikmen et al., 2019). Therefore, it is difficult to ascertain that descriptive and inferential statistics were used appropriately, and that the researchers were able to address the risks of statistical error and bias. I agree with you that inferential statistics has strengthened the descriptive statistics provided in the article, but I think that the researchers should have been more detailed describing their data analysis procedures. Do you agree?

References

Dikmen, Y., Filiz, N.Y., Tanrikulu, F., Yilmaz, D., & Kuzgun, H. (2019). Attitudes of

intensive care nurses towards evidence-based nursing. International Journal of Health Sciences & Research, 8(1), 138-143.

Shayan, S.J., Kiwanuka, F., & Nakaye, Z. (2019). Barriers associated with evidence-based

practice among nurses in low- and middle-income countries: A systematic review. Worldviews on Evidence-Based Nursing, 16(1), 12-20. https://doi.org/10.1111/wvn.12337

Weissberger, T.L., Garcia-Valencia, O., Garovic, V.D., Milic, N.M., & Winham, S.J. (2018).

Why we need to report more than ‘Data were analyzed by t-tests or ANOVA’. eLife, 7, e36163. https://doi.org/10.7554/eLife.36163

. To begin with, nurses are evidence-based practice (EBP) advocates and agents of evidence-based change in their clinical setting. I agree with you that nurses are also the largest health care workforce. Therefore, they can have a major impact on the provision of evidence-based care to patients. However, nurses can also face barriers to EBP in their practice setting (Shayan et al., 2019). The study you have chosen for this discussion is equally relevant and informative. On the one hand, it provides data to describe nurses’ attitudes toward EBP (Dikmen et al., 2019). On the other hand, it illustrates how descriptive and inferential statistics can be helpful in the analysis of correlations. Yet, how consistent is the statistical analysis performed in the study?

           As mentioned in your post, the researchers in your chosen study used both descriptive and inferential statistics. For the latter, Dikmen et al. (2019) used ANOVA, independent t-test, and Pearson correlation. Each of these have proved useful in the analysis of statistical correlations. However, it appears that researchers often fail to include essential information about the statistical tests used, thus limiting the transparency of their research procedures. Weissgerber et al. (2018) say that researchers should not only specify the type of ANOVA used (i.e., one- or two-way ANOVA) but also include the name and level for each factor, report the F-statistic, and mention if any post-hoc tests were performed.

The study mentioned in your post includes just a brief discussion of both the methods and results (Dikmen et al., 2019). Therefore, it is difficult to ascertain that descriptive and inferential statistics were used appropriately, and that the researchers were able to address the risks of statistical error and bias. I agree with you that inferential statistics has strengthened the descriptive statistics provided in the article, but I think that the researchers should have been more detailed describing their data analysis procedures. Do you agree?

References

Dikmen, Y., Filiz, N.Y., Tanrikulu, F., Yilmaz, D., & Kuzgun, H. (2019). Attitudes of

intensive care nurses towards evidence-based nursing. International Journal of Health Sciences & Research, 8(1), 138-143.

Shayan, S.J., Kiwanuka, F., & Nakaye, Z. (2019). Barriers associated with evidence-based

practice among nurses in low- and middle-income countries: A systematic review. Worldviews on Evidence-Based Nursing, 16(1), 12-20. https://doi.org/10.1111/wvn.12337

Weissberger, T.L., Garcia-Valencia, O., Garovic, V.D., Milic, N.M., & Winham, S.J. (2018).

Why we need to report more than ‘Data were analyzed by t-tests or ANOVA’. eLife, 7, e36163. https://doi.org/10.7554/eLife.36163

True, statistics are essential in quantitative research; they can also facilitate data analysis in a clinical setting. However, only when statistics are done appropriately, they can benefit science and advance nursing practice. Please let me add a few comments regarding the study you discussed in your post.

           To begin with, the article does not include a detailed description of statistics or data analysis. According to Katzmarzyk et al. (2019), the design and methods of the study had been discussed elsewhere. I found this information in an earlier publication. In it, Katzmarzyk et al. (2013) say that the study would involve descriptive and inferential statistics, namely, multilevel random-effects models and covariate-adjusted models. The choice of the model looks appropriate, since the ISCOLE study involves numerous independent variables, such as physical activity, dietary patterns, and so on. Complex statistical models are well-suited to analyze complex correlational or causal links among variables. However, they also have limitations. For example, they can be time consuming.

           The main question is whether the inferential statistics used in the study were sufficient and effective enough to identify cause-and-effect relationships. Katzmarzyk et al. (2019) note that their statistical models had limitations, making causal inferences problematic or questionable. They could have used ANOVA or t-test, but they would not be suitable in the analysis of causal relationships. According to Schober et al. (2018), correlations do not imply causality; nor do they say enough about the strength of the relationship between the independent and dependent variable. Besides, in the discussed study, data were collected at a single point of time, which could limit their utility in identifying cause-and-effect relationships.

Notwithstanding these limitations, the use of inferential statistics was beneficial, as it added to and expanded descriptive statistical results. The researchers were able to demonstrate the complexity of factors affecting childhood obesity rates across countries. They may still need additional data to address weaknesses in method and design, such as using other inferential statistics to validate the initial findings.

References

Katzmarzyk, P.T., Barreira, T.V., Broyles, S.T., Champagne, C.M., Chaput, J.P., Fogelholm,

M., Hu, G., Johnson, W.D., Kuriyan, R., Kurpad, A., Lambert, E.V., Maher, C., Maia, J., Matsudo, V., Olds, T., Onywera, V., Sarmiento, O.L., Standage, M., Tremblay, M.S., Tudor-Locke, C., Zhao, P., & Church, T.S. (2013). The International Study of Childhood Obesity, Lifestyle and the Environment (ISCOLE): design and methods. BMC Public Health, 13, 900. https://bmcpublichealth.biomedcentral.com/articles/10.1186/1471-2458-13-900

Katzmarzyk, P. T., Chaput, J. P., Fogelholm, M., Hu, G., Maher, C., Maia, J., Olds, T.,

Sarmiento, O.L., Standage, M., Tremblay, M.S., & Tudor-Locke, C. (2019). International Study of Childhood Obesity, Lifestyle and the Environment (ISCOLE): contributions to understanding the global obesity epidemic. Nutrients,11(4), 848. https://dx.doi.org/10.3390/nu11040848  

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

Since entering the career of nursing, I believe that most nurses would like to gather as much experience as they can to become a proficient and well-rounded staff in this profession. Being a nurse for about six years now, I have spent the last two and a half years working my way up to become an intensive care unit (ICU) nurse. Being an ICU nurse is a specialty in itself that provides many nursing with the competitive pay, comprehensive benefits, and extensive learning experience in critical level of care. As the ICU can be a stressful environment for patients and families, with established long term consequences, the impact that this unique environment can have on healthcare professionals is increasingly being recognized.

            What I have noticed while being a nurse in the critical care environment, I have noticed a significant increase in our nurse turnover rates for both local and traveling nurse staff. For as long as I have been working in this hospital (in a different unit at the time), many nurses are either not trained properly and/or experiencing burnout early on in their career due particularly in the ICU unit. The exposure of  nurses within a high acuity nursing environment without the proper support from our management has led to burnout. Furthermore, I have noticed that the ICU unit is the only unit with the least amount of local nurses that stay employed for at least two years into their career life.

Most of the staff nurses that I have worked with have expressed the desires to leave off-island in search for better opportunities or change in nursing career. Our hospital is going through a constant battle with recruiting and retaining their nursing staff, specifically more significant in the ICU unit. Our medical director is currently working alongside the hospital administrators about looking for ways to address the increase burnout that the staff nurses are experiencing and construct a resilient healthcare system. For as long as I have been working in this hospital (in a different unit at the time), many nurses are either not trained properly with the advanced skills needed dealing with life threatening illnesses and/or lack the skills to tackle critically ill conditions. As a result overall, burnout causes decrease in quality of care, poor performances, increase mortality in patients, and errors in the healthcare environment.

The impact that this unique environment can have on healthcare professionals is increasing therefore, as a DNP prepared nurse, to gain a more complete understanding of critical care well-being requires commitment to measure, develops interventions, and re-measure them. An analysis variation or ANOVA tests done for each survey or experimental results are significant and help us figure out if the studies prove our hypothesis. Inferential statistics takes data from samples and make generalizations about a population. Experimental analysis using t-test, to compare the means of two groups or ANOVA (analysis of variance) to analyze the difference between the means of more than two groups, would help make estimates about the population at study (nurses) and testing hypothesis to draw conclusions (Bhandari, 2020).

One of the chosen inferential articles that describe the prevalence of burnout in the ICU healthcare assessed in the included analysis of variance study (ANOVA) through PubMed, Medline, and a web of sciences article reviews and observational study designs. Within the articles, the most commonly used instruments for data collection include the Maslach burnout inventory (MBI), professional quality of life scale, work related behavior, and experience patterns. According to a 4 large scale research study reported that the burnout prevalence rates ranges between 28%-61%; this study suggests that ICU workers were slightly (about 20%) more prone to burnout than the average healthcare (Chuang, Tseng, Lin, Lin & Chen, 2016). The following risk factors reported include: age, sex, marital status, personality traits, work experiences, work environment, workload, shift work, ethical issues, and decision making choices.

In another article review done by Kerlin, McPeake & Mikkelsen (2020), being that ICU can be a stress environment for both patients and families; the impact that this environment can have on healthcare environment is being increasingly recognized. Challenging situations, exposure to high mortality and daily difficult workloads can lead to excessive stress and resultant in moral distress, leading to burnout syndrome. This cross-sectional study, most critical care nurses experience about 81% of one or more burnout symptoms. The framework presented in this article implies that multidisciplinary and coordinated cares are essential components to high quality critical care delivery. The publications are assessed for relevance to using data to support observational study designs that examine associations between any risk factors and burnout in the ICU setting.

In a systematic meta-analysis done by Ramirez-Elvira et.al. (2021), the ANOVA is  carried out with different articles and journals from Medline and CINAHL following the PRISMA (preferred reporting item for systematic reviews and meta-analysis), with a sampling of N= 1989; there was an estimate of about 31% prevalence for high emotional exhaustion, 18% high depersonalization, and 49% low personal achievement (p.2). Furthermore, in an inferential statistical cross-sectional total population study among N=60 nurses using a self-administered MBI questionnaire resulted into a high burnout percentage of about 62% (Cishahayo, Nankundwa, Sego, & Bhengu, 2017). Burnout is measured through high emotional exhaustion (48%), high depersonalization (25%), and low personal accomplishment (50%).

On a much larger and international scale, in study done by Bhagavathula et.al. (2018), an institution based teaching hospital with a cross-sectional study conduced among healthcare providers N=500 serving about >50,000 population in Ethiopia; a questionnaire with sociodemographic details using descriptive analysis using correlation and multivariate logistic regression studied ANOVA using survey questionnaires of MBI scale. The overall prevalence of burnout is about 14%, respondents with debility was 53%, increase self criticism of about 56%, and depressive symptoms of about 46%. As a result, the nursing profession was a significant determinant for emotional exhaustion and burnout.

In conclusion, most inferential studies summarized above strengthens the application of evidenced based practices in promoting recruitment and retention policies in decreasing the risk of burnouts. Critical care courses and educational programs should be established by the support faculty to meet the needs of critical care assessments and criteria. Practice variability that necessitates changing for better conditions in a resource limited setting may excavate the underlying factors associated with nursing burnout.

Reference(s):

Bhagavathula, A., Abegaz, T., Belachew, S., Gebreyohannes, E., Gebresillassie, B., & Chattu, V.

(2018). Prevalence of burnout syndrome among healthcare professionals working at Gondar University Hospital, Ethiopia. Journal of Educational Health Promotion 7(145). Retrieved from https://www.jehp.net/article.asp?issn=2277-9531;year=2018;volume=7;issue=1;spage=145;epage=145;aulast=Bhagavathula

Bhandari, P. (2020). An introduction to inferential statistics. Scribbr Statistics. Retrieved

https://www.scribbr.com/statistics/inferential-statistics/

Chuang, C., Tseng, P., Lin, C., Lin, K. & Chen, Y. (2016). Burnout in the intensive care unit

professionals. Medicine Baltimore Journal. 95(50). Retrieved from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5268051/

Cishahayo, E., Nankundwa, E., Sego, R., & Bhengu, B. (2017). Burnout among nurses working

in critical care settings: A case of a selected tertiary hospital in Rwanda. International Journal of Research in Medical Sciences. 5(12). Retrieved from https://www.msjonline.org/index.php/ijrms/article/view/4101

Kerlin, M., Mc Peake, J., & Mikkelsen, M. (2020). Burnout and joy in the profession of critical

care medicine. BMC Critical Care Journal. 24(98). Retrieved from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7092567/

Ramirez-Elvira, S., Romero-Bejar, J., Suleiman-Martos, N., Gomez-Urquiza, J., Monsalve-

Reyes, C., Canadas-Delafuentes, G…Albendin-Garcia, L. (2021). Prevalence, risk factors, and burnout levels in intensive care unit: A systematic review and meta-analysis. International Journal of Environmental Research and Public Health. 18.Retrieved from www.mdpi.com/pdf