A causal model for @entity2 XXXX receiving chemotherapy.

@entity1 @entity1353 @entity2 @entity1295 @entity148 @entity22
PURPOSE: This study explored a causal model explaining @entity2 @entity1 currently undertaking Chemotherapy. METHOD: In this cross-sectional study, a convenience sample of 246 @entity2 @entity1 were recruited from six Oncology centers throughout the north of Vietnam. The hypothesized model, consisting of @entity22 , @entity1353 , cough, @entity148 , stage of disease, physical activity, nutritional status, and number of completed chemotherapy cycles, was constructed based on Piper's Integrated Fatigue Model and a review of the literature. RESULTS: All factors, except the number of completed chemotherapy cycles significantly affected @entity1295 . The hypothesized model explained 42.9% of @entity1295 variance. @entity1353 had the largest total effect on @entity1295 (b  =  0.397, p  <  0.01), followed by cough (b  =  0.343, p  <  0.01), @entity22 (b  =  0.318 (p  <  0.01), and @entity148 (b  =  0.115, p  <  0.05). However, @entity22 had the greatest direct effect on @entity1295 . There was also interplay among those four factors in determining @entity1295 . Physical activity and nutrition status had small effects on @entity1295 (b  =  - 0.148, p  <  0.01 and b  =  - 0.156, p  <  0.01). CONCLUSION: The model fits well to explain @entity1295 . Having the largest direct effect on @entity1295 , @entity22 appeared as a factor of choice for future @entity1295 control programs. Due to its higher direct effect on @entity1295 , @entity1353 was recommended over cough for @entity1295 management. Additionally, the interactions among @entity1295 , @entity1353 , and cough suggested that comprehensive programs, which simultaneously address these three symptoms, would be a promising approach for practitioners to consider.
