Journal Article Research Critique Essay Example

study should have included a clear hypothesis that identified the expected direction of the research. Did the researchers anticipate finding specific symptom clusters unique to individuals with a history of cancer? Or did they expect that the symptom clusters would be more severe in cancer survivors compared to those without a history of cancer, who also have chronic health problems? The hypothesis could have been stated as follows: there will be no significant difference in symptom clusters between patients with a previous cancer diagnosis and chronic health issues, compared to patients with chronic health problems but no history of cancer. It is also important for the hypothesis to specify the parameters needed for statistical significance. While Bender et al. (2008) extensively explain the statistical processes used in their exploratory endeavor, they do not mention whether these parameters were established prior to starting the study, which would have been appropriate. The literature search supports the research goal.

According to a review, individuals who have undergone cancer treatment experience significant symptoms. This finding is supported by several studies. One particular study (Reyes-Gibby et al., 2006) compared the symptom experiences of cancer patients with those of individuals suffering from other chronic health issues. However, it did not specify the specific comorbid conditions that may contribute to these symptom clusters.

To further investigate this matter, it would be reasonable to conduct retrospective studies on cohorts that include both cancer patients and individuals with chronic health problems. The initial data collection utilized the Comorbidity Questionnaire, which was based on the Charlson Comorbidity Index (CCI) as described by Bender et al. (2008).

The modified tool used in this study was not disclosed, and the

author could not obtain a copy of it. However, it is important to note that the tool was approved for use by the National Institutes of Health. On the other hand, the CCI (Hall, Ramachandran, Narayan, Jani, & Vijayakumar, 2004) is a reliable and validated tool. Therefore, the author cannot assess the validity and reliability of the initial data collection tool. One can infer that if the CCI is valid and reliable, then the modified tool should also be valid and reliable. However, making such an assumption is not proper protocol.

The statistical analysis conducted in this study included appropriate tests for a non-parametric study: Kruskal-Wallis test, Chi-square test, and Fisher exact test (Bender et al., 2008,p.E3).

The data was analyzed using SPSS® and Mplus®, with Bender et al. (2004) providing detailed information about the tests and their functions in relation to this study. Despite outlining the statistical processes and outcomes, the group did not mention the p value (p=0.05 as described in the analysis) in a hypothesis. Hence, it is unclear whether this parameter was predetermined before the trial or was determined after knowing the results.

The former, naturally, is inappropriate. The Chi-Squared test examines the comparison between attributes in a sample to identify any relationship that can then be used for predicting occurrence frequency. The Kruskal-Wallis Test is utilized in analyzing variance in situations that are not necessarily normally distributed (Easton and McColl, 1997). The Fisher Exact Test is employed to determine nonrandom associations between two variables with sparse cell sizes (Weinstein, 2008). If one had been established, the test results would have led the researchers to accept a null hypothesis. In addition, questions arise regarding

the sample used because it was self-reported and the method for selecting individuals to receive the tool was not clarified.

The study did not provide information on the number of eligible participants compared to those who actually participated, which raises concerns about its validity. The majority of the sample consisted of Caucasian individuals who had medical insurance. This lack of diversity in the sample pool may introduce bias into the results.

Bender, et al found that a significant percentage (81%) of participants with RA were female and white (92%), which aligns with the overall population affected by RA. According to Rheumatoid Arthritis (2004) from the National Institute of Arthritis and Musculoskeletal and Skin Diseases (NIAMS) and the National Institutes of Health (NIH), individuals from all racial and ethnic backgrounds can be affected by RA, although women are more commonly affected than men.

Bender et al. (2008) noted that the study sample had a higher percentage of women (1%) and Caucasian individuals (92%) compared to the overall population surveyed. Additionally, the sample had a significant percentage (99.8%) of individuals with insurance coverage. This finding implies that those without insurance may experience distinct symptoms related to concerns about quality of life. It is crucial to recognize this variable, as stressed by Bender et al.

(2008) Bender et al. identified flaws in the study and acknowledged several trends. They concluded that the lack of clear differences in symptom clusters between the two groups suggests that these experiences may be influenced more by main chronic health problems and other coexisting conditions in the subjects (Bender et al., p. E6). The authors also recognized potential confounding variables, such as variations in timing of symptom

assessment, common symptoms among the elderly, participants' age and their expectations regarding diseases, limited information about cancer, and educational background of individuals. In summary, Bender et al.'s study suggests that symptom clusters are closely associated with primary chronic health issues and other coexisting conditions rather than any group differences.

In a 2008 study, researchers called for further investigation into symptom clusters in cancer patients and their connection to other chronic health issues. However, the author of this passage agrees that more studies are needed but suggests a different focus. One potential area of exploration is whether breast cancer patients who receive chemotherapy have a higher likelihood and severity of developing rheumatoid arthritis (RA) compared to non-breast cancer patients with a predisposition for RA who eventually develop it. The author recommends conducting a prospective study instead of relying on biased data to address these important questions.

References

1. Bender, Catherine M., Engberg, Sandra J., Donovan, Heidi S., Cohen, Susan M., Houze, Martin P., Rosenzweig, Margaret Q., Mallory, Gail A.,
   Dunbar-Jacob Jacqueline,& Sereika Susan M. (2008, January).

The article "Symptom clusters in adults with chronic health problems and cancer as a comorbidity" can be found in the Oncology Nursing Forum, specifically in volume 35 on pages 1-11. It is available online and was obtained from the Metapress database on February 3, 2008. Additionally, there is a glossary on statistics created by Valerie J. Easton and John J. McColl in September 1997, which can be accessed at http://www.

stats. gla.ac.uk/steps/glossary/nonparametric. HTML

BMC Cancer, 4:94. Retrieved February 3, 2008, from: http://www.biomedcentral.com/1471-2407/4/94

The website http://www.iams.nih.gov/Health_Info/Rheumatic_Disease/default contains information on rheumatic disease. This information was retrieved on March 6, 2008.

asp#ra_3

A. , Anderson, K.O. , Mendoza, T. R.

& Cleeland, C.S. (2006). Pain, depression, and fatigue in community-dwelling adults with and without a history of cancer. Journal of Pain and Symptom Management, 32(2), 118-128.
Weisstein, Eric W. (2008). Fisher's exact test.

The source URL for MathWorld, a Wolfram Web Resource, is http://mathworld.wolfram.com/FishersExactTest.html. Cheryl O'Neal retrieved this information on March 7, 2008.