Symptom Clusters in Breast Cancer Survivors: A Latent Class Profile Analysis
نویسندگان
چکیده
منابع مشابه
Older breast cancer survivors' symptom beliefs.
PURPOSE/OBJECTIVES To use Leventhal's Common Sense Model (CSM) to describe older breast cancer survivors' symptom representations, symptom management strategies, and perceived barriers to symptom management. DESIGN A secondary analysis was conducted using data from three pilot studies that tested a theory-based intervention to improve symptom management in older breast cancer survivors. SET...
متن کاملSexual Dysfunction in Breast Cancer Survivors
Background: Approximately 12.3 percent of women will be diagnosed with breast cancer at some point during their lifetime. Breast cancer is accompanied by alternation in body image and worries about sexual attractiveness. Thus, sexual life of breast cancer survivor’s needs special attention. This study aimed to evaluate the effects of breast cancer on women’s sexual function. ...
متن کاملSymptom clusters and quality of life in older adult breast cancer survivors.
PURPOSE/OBJECTIVES To identify symptom clusters in older adult breast cancer survivors (ages 65-97 years) and examine whether symptom clusters are related to demographic, health, and quality-of-life variables. DESIGN Factor analysis to identify possible symptom clusters. The resulting clusters then were correlated with quality-of-life measures. SETTING Phone interviews between the participa...
متن کامل[Symptom experience and quality of life in breast cancer survivors].
PURPOSE The purposes of this study were to evaluate symptom experience and quality of life (QOL) and to identify the predictors of QOL among breast cancer survivors. METHODS A cross-sectional study was conducted on 200 disease-free breast cancer survivors at two hospitals between December 2007 and July 2008. Functional Assessment of Cancer Therapy Scale-B, Memorial Symptom Assessment Scale-sh...
متن کاملMixture models: latent profile and latent class analysis
Latent class analysis (LCA) and latent profile analysis (LPA) are techniques that aim to recover hidden groups from observed data. They are similar to clustering techniques but more flexible because they are based on an explicit model of the data, and allow you to account for the fact that the recovered groups are uncertain. LCA and LPA are useful when you want to reduce a large number of conti...
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ژورنال
عنوان ژورنال: Oncology Nursing Forum
سال: 2020
ISSN: 0190-535X,1538-0688
DOI: 10.1188/20.onf.89-100