Methods and Analysis
Research Design
The projects will use a qualitative research design in determining how hemodialysis influences the quality of health among patients with end-stage renal disease (ESRD). Qualitative research design is advantageous because it allows researchers to measure and quantify variables of interest, and thus, providing robust data for statistical analysis. Moreover, quantitative research design is valuable for it permits researchers to define dependent and independent variables, which are key variables that are essential in hypothesis testing and determining the statistical significance of relationships. Since nursing care determines the nature of palliative care that patients with ESRD receive, nurses play a significant role in influencing the quality of life.
Thus, the assessment of the roles of nurses in the provision of palliative care and the nature of palliative care reflects how hemodialysis influences the quality of life. In the quantitative research design, the study will measure both the dependent and the independent variables of the study. In the survey, the dependent variable is the EQ-5D-5L value, which is a composite value emanating from the sum of mobility, self-care, usual activities, pain, and depression scores (Zyoud et al., 2016).
Moreover, the survey has independent variables, which comprise various aspects of palliative care that nurses provide. Therefore, the assessment of the influence of the various aspects of palliative care on the quality of life among patients with ESRD provides important information regarding the roles of nurses in the provision of palliative care.
In this project, the study will undertake surveys to determine if hemodialysis has a negative or a positive influence on the quality of life among patients with ESRD. The study will design the questionnaire and use it in collecting relevant data from the participants. To quantify the variables, the study will use established scales, which measure palliative care and the quality of life among patients with ESRD.
In essence, the surveys will have Likert items that measure each attribute of quality of life among patients with ESRD. The dependent variable of the study is EQ-5D-5L scores, which measure the quality of life among patients in response to the palliative care they receive as patients with ESRD (Zyoud et al., 2016). The independent variables of the study are palliative care offered by nurses in the aspects of the manner communication, advocacy for patients, compassionate care, physical and mental care, advocacy for resources, and bereavement and grief support to patients and families (Kelley & Morrison, 2015).
Thus, quantification of these variables using an established scale of the EQ-5D-5L index and Likert items of palliative care provides a robust way of assessing the influence of palliative care on the quality of life of patients with ESRD.
Population
The target population of the study is patients with ESRD who receive palliative care from dialysis centers across the state. Since dialysis centers provide palliative care to patients with ESRD, they offer the right target population to the study. In this view, the nature of palliative care that patients with ESRD get in dialysis centers determines the quality of life. Essentially, the number of patients in various dialysis centers varies according to the availability of facilities and resources to provide palliative care to patients with ESRD.
According to Saad et al. (2015), palliative care should provide comprehensive care services that meet the social, physical, and mental needs of patients so that they can live a quality life worthy of living. In this view, patients with ESRD in various dialysis centers constitute the appropriate population for the study of the influence of palliative care on the quality of life.
Inclusion and Exclusion Criteria
For patients with ESRD who receive palliative have different demographic variables and health conditions, the study will use inclusion and exclusion criteria in selecting the appropriate participants. The first inclusion criterion is that the participants ought to have confirmed the diagnosis of ESRD with a medical history. The second inclusion criterion is that the participants must be adults, both males, and females, aged between 18 and 65 years. The third inclusion criterion is that the participants should have been on regular hemodialysis for the past period of six months. The first exclusion criterion is that the participants should not have the ages below 18 years and above 65 years.
The second exclusion criterion is that the participants ought to have a physical and psychological capacity to answer survey questions and provide valid and reliable information about their health and palliative care they receive. The third exclusion criterion is that the study excluded patients with additional chronic conditions for they confound the findings of the study. Therefore, the target population will be subjected to the inclusion and the exclusion criteria to obtain the appropriate participants of the study.
Recruitment of Participants
Since patients with ESRD are few, the study will employ a convenience method of sampling, which is a non-probability method of sampling, in the recruitment of participants. Convenience sampling is appropriate in instances where the target population is small and a researcher wants to enhance the representation of the sample (Field, 2013).
Thus, by using the inclusion and exclusion criteria, the study will select participants who are confirmed patients with ESRD, adults aged between 18 and 65 years, and those who have been active on hemodialysis in the previous six months without the capacity to answer survey questions. Before the recruitment of participants, the study examined records of the ministry of health and assessed the number of patients undertaking hemodialysis regularly in various dialysis centers.
Sample Size
The study will sample 139 participants from various dialysis centers, which is the appropriate number of patients to represent patients with ESRD. The study used Cochran’s formula in determining the sample size of participants. Using the formula of sample size with indefinite population, n = (Z/E)2 pq where n is the sample size, Z is the critical value for 95% confidence interval, E is the margin of error, p is the proportion of responses in a population, and q proportion of non-responses in a population (Sugathan & Benny, 2015). With a Z value of 1.96, a E value of 0.05, a p value of 0.9, and a q value of 0.1, the n is about 139 (n = (1.96/0.05)2 × 0.9 × 0.1 = 138.3).
The sample of 139 participants is justifiable because the margin of error is low (5%), the confidence level is high (95%), and the response rate of surveys is reasonable (0.9). The study has no control group because it aims to determine how various aspects of palliative care influence the quality of life among patients with ESRD. Essentially, the study aims to establish the relationship between palliative care and the quality of life and the extent to which various aspects of palliative care influences the quality of life.
Study Settings
The settings of the study will be dialysis centers where patients with ESRD. Since the study does not want to interfere with the settings of patients, it will undertake surveys at the hospital settings where patients receive respective palliative care. The hospital setting is relevant in this study because it provides resourceful support to researchers during the survey. Nurses and other healthcare providers in dialysis centers enable researchers to create rapport with patients, which is integral in promoting their participation in the study. Besides, nurses and other healthcare providers assist researchers in undertaking surveys and collecting data. Since nurses provide palliative care to patients with ESRD, they offer required information regarding various aspects of palliative care.
Protection of Participants
The protection of participants will be made possible by seeking ethical clearance from the Institutional Ethics Committees from various dialysis centers. The Institutional Ethics Committees will review the proposal and provide recommendations and guidelines, which will ensure the protection of participants. In line with ethical requirements, the study will seek written informed consent from participants. Moreover, the study will ensure that there is voluntary participation by not coercing or enticing patients with ESRD to participate in the study.
During the survey, the study will use questionnaires with moderated questions to avoid asking sensitive questions that would hurt participants. Since the study survey to collect data regarding palliative care and the quality of life, the study will protect the data by treating them confidential to avoid leakage and usage in victimization or stigmatization of participants. In this view, data protection will entail coding and securing data to prevent access and use by unauthorized people.
Data Analysis
The study will use Statistical Package for the Social Sciences (SPSS) as a statistical program in undertaking data analysis. SPSS allows researchers to quantify responses obtained from respondents, and thus, convert qualitative data into numeric data, which is necessary to undertake the quantitative analysis. Essentially, SPSS is a versatile and powerful program for statistical analysis because it allows coding of data, computation of variables, recording of data, the performance of descriptive statistics, and analysis of inferential statistics.
In this view, the study will collect data and record them in SPSS program. In the aspect of palliative care, the study will take each question as a variable of palliative care and use it in predicting the quality of life. In quantifying the quality of life, the study will compute the variable of quality of life by summing up the ratings of the five Likert items of the EQ-5D-5L scale. The computation of the quality of life provides an accurate and reliable dependent variable, which measures the quality of life among patients with ESRD.
In data analysis, the study will analyze descriptive statistics to determine patterns and trends of the demographic variables of participants with ESRD. The descriptive statistics will provide distribution of participants regarding gender, age, duration of sickness, and duration of hemodialysis. The descriptive statistics provide the basis for interpreting palliative care and the quality of life among patients with ESRD. Furthermore, descriptive statistics will provide the extent of palliative care provided to patients with ESRD and the quality of life they live. In this view, mode, median, and mean are key statistical parameters that the study will consider in the interpretation of data.
To determine if hemodialysis has a negative or a positive influence on the quality of life, the study will perform correlation analysis. The study will determine how each parameter of palliative care correlate with the quality of life among patients with ERSD. According to Yusop, Mun, Shariff, and Huat (2013), the treatment and management of ESRD require the use of multidisciplinary approaches to improve the quality of life among patients. Correlation analysis is appropriate in determining the strength and the direction of the relationship between two numeric variables. Since parameters of palliative care and the quality of care are on continuous scales, correlation analysis is possible. A positive correlation implies that palliative care improves the quality of life while a negative correlation means that palliative care reduces the quality of life.
Subsequently, the study will undertake regression analysis as inferential statistics in determining if parameters of palliative care are statistically significant predictors of quality of life among patients with ESRD. Regression analysis will establish the degree of causal relationship between the independent variable and the dependent variable. In this case, parameters of palliative care are the independent variables whereas the quality of life is a dependent variable. Regression analysis will offer R-square, which is a coefficient showing the extent to which a parameter of palliative care accounts for the variation in the quality of life.
Moreover, regression analysis will provide ANOVA test, which indicates statistical significance of the regression model employed in predicting the relationship between a parameter of palliative care and the quality of life. Ultimately, the regression model will provide coefficients of predictors (parameters of palliative care), which reflect the regression equations of the relationships between parameters of palliative care and the quality of life.
Project’s Timeline
The table below illustrates the timeline of the project within a period of 7 weeks.
References
Field, A. (2013). Discovering statistics using IBM SPSS statistics. Los Angeles, CA: SAGE Publisher.
Kelley, A. S., & Morrison, R. S. (2015). Palliative care for the seriously ill. New England Journal of Medicine, 373(8), 747-755.
Saad, M., El Douaihy, Y., Boumitri, C., Rondla, C., Moussaly, E., Daoud, M., & El Sayegh, S. E. (2015). Predictors of quality of life in patients with end-stage renal disease on hemodialysis. International Journal of Nephrology and Renovascular Disease, 8(1), 119–123. Web.
Sugathan, S., & Benny, P. (2015). Biostatistics in a nut shell for medical researchers. New York, NY: Educreation Publishing.
Yusop, N., Mun, C., Shariff, Z., & Huat, C. (2013). Fcators associated with quality of life among hemodialysis patients in Malaysia. PLoS ONE, 8(12), 1-11. Web.
Zyoud, S., Daraghmeh, D., Mezyed, D., Khdeir, R., Sawafta, M., Ayaseh, N.,… Al-Jabi, S. (2016). Factors affecting quality of life in patients on haemodialysis: a cross-sectional study from Palestine. BMC Nephrology, 17(44), 1-12. Web.