Cancer Patients Satisfaction and Quality of Healthcare Services in Iraq: A Cross-Sectional Study to Evaluate the Quality of Care in Cancer Management.

OBJECTIVE: Cancer is a complex disease characterized by uncontrolled cell proliferation and the development of metastatic features. The aim of the study is to examine the patient's satisfaction with the quality of healthcare services provided at the Middle Euphrates Cancer Centre in Al-Najaf Al-Ashraf Governorate. METHODS: Cancer patients who visited during 2021-2023 Middle Euphrates Cancer Center in Al-Najaf Al Ashraf Governorate in 2021-2023 were enrolled in the study. In the cross sectional study, enrolled cancer patients were screened based on inclusion and exclusion criteria. In this study, cancer patient satisfaction assessment was made based on responses from a 59 items questionnaire. RESULTS: In the study period, 400 cancer patients who visited the Middle Euphrates Cancer Center in Al-Najaf Al Ashraf Governorate enrolled in the study. Cancer patient's satisfaction was assessed based on the care provided by physicians, nurses, the infrastructure of the organization, and their socioeconomic status. Under the category of care provided by the physician, the level of assessment reported was low [L] =1-2.33; moderate [M] =2.34-3.66; 2.34-3.66, and high [H] =3.67-5). However, in the case of care provided by nurses, the level of assessment is low ([L] =1-2.33; moderate [M]=2.34-3.66; high [H]=3.67-5.0). The level of assessment (low [L] =1-2.33; moderate [M] = -3.66; high [H]=3.67-5) at the organization level for the services and facilities. CONCLUSION: Findings clearly demonstrate that the participants were dissatisfied with some services provided by doctors, nurses, or organizations. The findings also emphasize the critical need to tailor healthcare services, enhance accessibility, and elevate the overall quality of care to enhance patient satisfaction significantly.

Organizational culture and the individuals' discretionary behaviors at work: a cross-cultural analysis.

Stating "how things are done here," organizations are defining their culture. Organizational Culture (OC) is the set of values, norms, goals, and expectations shared by all members of an organization that aids in improving their commitment and performance. On the organizational level, it impacts behavior, productivity, and long-term survival by influencing organizational capability. Due to employee behavior being a competitive differential, this study examines how specific OCs influence individual behavior. In particular, how the different cultures in the Organizational Culture Assessment Instrument (OCAI) affect employees' main dimensions of Organizational Citizenship Behavior (OCB). A descriptive-confirmative ex post facto research was conducted by surveying 513 employees from over 150 organizations worldwide. The Kruskal-Wallis H-test was used to validate our model. The general hypothesis was confirmed, showing that the predominant organizational culture type affects the level and the kind of OCBs individuals demonstrate. It is possible to provide organizations with a breakdown of their employees' OCBs based on their OC type and which changes they can make to their organization's culture to increase the employees' OCB and, consequently, the efficiency of their organization.

Analysis of COVID-19 data using neutrosophic Kruskal Wallis H test.

BACKGROUND: Kruskal-Wallis H test from the bank of classical statistics tests is a well-known nonparametric alternative to a one-way analysis of variance. The test is extensively used in decision-making problems where one has to compare the equality of several means when the observations are in exact form. The test is helpless when the data is in an interval form and has some indeterminacy. METHODS: The interval-valued data often contain uncertainty and imprecision and often arise from situations that contain vagueness and ambiguity. In this research, a modified form of the Kruskal-Wallis H test has been proposed for indeterminacy data. A comprehensive theoretical methodology with an application and implementation of the test has been proposed in the research. RESULTS: The proposed test is applied on a Covid-19 data set for application purposes. The study results suggested that the proposed modified Kruskal-Wallis H test is more suitable in interval-valued data situations. The application of this new neutrosophic Kruskal-Wallis test on the Covid-19 data set showed that the proposed test provides more relevant and adequate results. The data representing the daily ICU occupancy by the Covid-19 patients were recorded for both determinate and indeterminate parts. The existing nonparametric Kruskal-Wallis H test under Classical Statistics would have given misleading results. The proposed test showed that at a 1% level of significance, there is a statistically significant difference among the average daily ICU occupancy by corona-positive patients of different age groups. CONCLUSIONS: The findings of the results suggested that our proposed modified form of the Kruskal-Wallis is appropriate in place of the classical form of the test in the presence of the neutrosophic environment.

[Influence of group sample size on statistical power of tests for quantitative data with an imbalanced design].

OBJECTIVE: To explore the relationship between sample size in the groups and statistical power of ANOVA and Kruskal-Wallis H test with an imbalanced design. METHODS: The sample sizes of the two tests were estimated by SAS program with given parameter settings, and Monte Carlo simulation was used to examine the changes in power when the total sample size varied or remained fixed. RESULTS: In ANOVA, when the total sample size was fixed, increasing the sample size in the group with a larger mean square error improved the statistical power, but an excessively large difference in the sample sizes between groups led to reduced power. When the total sample size was not fixed, a larger mean square error in the group with increased sample size was associated with a greater increase of the statistical power. In Kruskal-wallis H test, when the total sample size was fixed, increasing the sample size in groups with large mean square errors increased the statistical power irrespective of the sample size difference between the groups; when total sample size was not fixed, a larger mean square error in the group with increased sample size resulted in an increased statistical power, and the increment was similar to that for a fixed total sample size. CONCLUSIONS: The relationship between statistical power and sample size in groups is affected by the mean square error, and increasing the sample size in a group with a large mean square error increases the statistical power. In Kruskal-Wallis H test, increasing the sample size in a group with a large mean square error is more cost- effective than increasing the total sample size to improve the statistical power.

Influence of group sample size on statistical power of tests for quantitative data with an imbalanced design / 南方医科大学学报

OBJECTIVE@#To explore the relationship between sample size in the groups and statistical power of ANOVA and Kruskal-Wallis test with an imbalanced design.@*METHODS@#The sample sizes of the two tests were estimated by SAS program with given parameter settings, and Monte Carlo simulation was used to examine the changes in power when the total sample size varied or remained fixed.@*RESULTS@#In ANOVA, when the total sample size was fixed, increasing the sample size in the group with a larger mean square error improved the statistical power, but an excessively large difference in the sample sizes between groups led to reduced power. When the total sample size was not fixed, a larger mean square error in the group with increased sample size was associated with a greater increase of the statistical power. In Kruskal-wallis test, when the total sample size was fixed, increasing the sample size in groups with large mean square errors increased the statistical power irrespective of the sample size difference between the groups; when total sample size was not fixed, a larger mean square error in the group with increased sample size resulted in an increased statistical power, and the increment was similar to that for a fixed total sample size.@*CONCLUSIONS@#The relationship between statistical power and sample size in groups is affected by the mean square error, and increasing the sample size in a group with a large mean square error increases the statistical power. In Kruskal-Wallis test, increasing the sample size in a group with a large mean square error is more cost- effective than increasing the total sample size to improve the statistical power.