The purpose of this study was to understand the effects of demographic characteristics and symptom distress of uncertainty in patients with cancer. Cross-sectional and correlational study was conducted. One hundred subjects, of a hospital in Eastern Taiwan were recruited, used convenience sampling. These subjects were divided into two groups, pain (n=50) and non-pain groups (n=50). A structured questionnaire and in-depth interview were used to assess the pain of patients with cancer. Correlational analysis was used to analyze data, using SPSS 10.0 software. The results indicated that, regarding uncertainty, there was a statistically significant difference between pain and non-pain groups (t=2.78, p< 0.00). Furthermore, regarding symptom distress, there was also a statistically significant difference between the two groups (t=-1.36, p<0.00). Symptom distress that explained 31% variances in uncertainty was the most predictive variable to predict uncertainty in the whole groups. Based on the findings of this study, health providers develop interventions to decrease patients' uncertainty and symptom distress. Hopefully, their Quality of life could be improved. (Tzu Chi Nursing Journal, 2004; 3:1, 72-80.)
The purpose of this study was to understand the effects of demographic characteristics and symptom distress of uncertainty in patients with cancer. Cross-sectional and correlational study was conducted. One hundred subjects, of a hospital in Eastern Taiwan were recruited, used convenience sampling. These subjects were divided into two groups, pain (n=50) and non-pain groups (n=50). A structured questionnaire and in-depth interview were used to assess the pain of patients with cancer. Correlational analysis was used to analyze data, using SPSS 10.0 software. The results indicated that, regarding uncertainty, there was a statistically significant difference between pain and non-pain groups (t=2.78, p< 0.00). Furthermore, regarding symptom distress, there was also a statistically significant difference between the two groups (t=-1.36, p<0.00). Symptom distress that explained 31% variances in uncertainty was the most predictive variable to predict uncertainty in the whole groups. Based on the findings of this study, health providers develop interventions to decrease patients' uncertainty and symptom distress. Hopefully, their Quality of life could be improved. (Tzu Chi Nursing Journal, 2004; 3:1, 72-80.)