Introduction
In an article by Lee et al (2011), patients diagnosed with lung cancer were taken through an empirical assessment on how they could improve their quality of life despite deteriorating health. The main benefit of the study to participants was that the psychometric health profile measure would indeed be instrumental in improving their quality of life even as they continue to receive treatment. In addition, the patients would be allowed to choose a particular health status after their conditions have been assessed thoroughly in the research study.
The participants in the study would also be in a position to understand their quality-adjusted survival years when their survival times are statistically weighed. Although the researchers failed to indicate any possible negative effects to participants who took part in the study, it is apparent that the entire process was safe both technically and health-wise. Participants were only required to fill out questionnaires and give feedback to interview questions. There were no medical tests to be taken.
Before the process of data collection, informed consent was obtained from each of the enrolled participants. In addition, the subjects were not compelled to take part in the study bearing in mind that the response rate was 91 %. It is also interesting to mention that the Institutional Review Board of the NTUH approved the quantitative research study before it was undertaken.
Collection of data
Both the dependent and independent variables have been identified in the research article. The dependent variables were the utility values. On the other hand, radiation therapy, chemotherapy, surgery, personal illness, marriage, smoking, religion, employment, education, age, and gender comprised the independent variables. However, the authors have not defined these variables thereby making it cumbersome for a novice audience to grasp the latent details.
Two main methods were used to collect data in the study. These included questionnaires and face-to-face interviews. Several questions were asked from patients. For instance, they were supposed to give feedback on the presence of specific medical conditions such as diabetes mellitus, hypertension, stroke, and heart diseases.
Besides, a standard gamble utility measurement was used to gather additional details. Nevertheless, the authors did not expound or give reasons for the chosen methods of data collection. The period for data collection in the study lasted for about five months (late August 2001 to January 2002). To gather the required data, a particular sequence was followed by the researchers. To begin with, participants above the age of 20 years were targeted for the study.
Approval was then obtained from the review board followed by consent from participants. Questionnaires were filled out by each of the respondents and finally, verbal interviews were carried out. A total of 220 healthy controls from NHIS were used for the sake of creating a comparison with the gathered data from the field. Although these were model methods for gathering data, the researchers did not point out how remote participants could be reached. For instance, there is no evidence of telephone interviews for respondents located in distant locations.
Data Management and Analysis
After data was collected from the participants and the relevant health departments, it was followed by the analysis process. To manage research data from the interviews, the SG utility values were elicited using the top-down titration method. This analysis method assisted in creating a comparison between the states of a participant’s health to a gambled value.
This analysis method made use of probabilities. A descriptive analysis of the gathered sample was also carried out after tabling the demographic characteristics of the participants. A summary of the various QOL domains was also conducted both for healthy controls and NSCLC patients. A calculation for the effect size about domain scores was also part and parcel of data management and analysis. From the summary score, the researchers constructed several linear regression models.
Individual facets and domains were represented by the models. The rigor of the process was assured using statistical analysis even though there is no evidence if statistical software was used in the analysis. Performing regression analysis is a thorough process that should utilize statistical software.
To minimize researcher bias during the selection process of participants, patients with sufficient cognitive ability and physical strength who had also attended lung cancer facilities between July and October 2002 were allowed to take part in the study.
Findings/interpretation of Findings
The researchers found out that poorer scores were common among participants with progressed stages of NSCLC especially when compared to the healthy controls. Besides, long-term cancer patients recorded lower quality of life than those who had been diagnosed recently.
As expected, the findings depict the reality because the Quality of Life (QOL) standards tend to diminish among patients who have struggled for long with specific complications. The cross-sectional nature of one healthcare facility was the main limitation to the quantitative study. In addition, a random sampling strategy was not used. Hence, the entire NSCLC patient population could not be represented. Nonetheless, coherent logic is evident in the presentation of findings.
The nursing profession can benefit from several findings in this study. For instance, optimal care for patients with all forms of cancer and other illnesses can indeed improve the quality of life. The study can indeed be applied to general nursing practice. According to the researchers, QOL scores demand further studies especially in regards to potential estimators of lung cancer to assess survival rates and disease progression.
References
Lee, L. J., Chung, C., Chang, Y., Lee, Y., Yang, C., Liou, S., Liu, P. & Wang, J. (2011). Comparison of the quality of life between patients with non-small-cell lung cancer and healthy controls. Quality of Life Research, 20(3), 415-423.