Dokumentdetails
ID
doi:10.1007/s10865-023-00422-w...
Autor
Coutts-Bain, D Sharpe, Louise Russell, HLangue
enEditor
Springer
Kategorie
Medicine & Public Health
Jahr
2023
Auflistungsdatum
14.06.2023
Schlüsselwörter
fear of cancer recurrence fear of progression death anxiety cognition psycho-oncology cancer fop recurrence ovarian progression variance variables fear theoretical death cancer anxietyZusammenfassung
Death anxiety is understudied in people with cancer, especially in relation to fear of cancer recurrence (FCR) and fear of progression (FOP).
The present study aimed to identify if death anxiety can predict FCR and FOP over and above other known theoretical predictors.
One hundred and seventy-six participants with ovarian cancer were recruited for an online survey.
We included theoretical variables, such as metacognitions, intrusive thoughts about cancer, perceived risk of recurrence or progression, and threat appraisal, in regression analyses to predict FCR or FOP.
We investigated whether death anxiety added to the variance over and above these variables.
Correlational analyses demonstrated that death anxiety is more strongly associated with FOP than FCR.
The hierarchical regression including the theoretical variables described above predicted 62–66% of variance in FCR and FOP.
In both models, death anxiety predicted a small but statistically significant unique variance in FCR and FOP.
These findings draw attention to the importance of death anxiety in understanding FCR and FOP in people with a diagnosis of ovarian cancer.
They also suggest that elements of exposure and existentialist therapies may be relevant in treating FCR and FOP.
Coutts-Bain, D,Sharpe, Louise,Russell, H, 2023, Death anxiety predicts fear of Cancer recurrence and progression in ovarian Cancer patients over and above other cognitive factors, Springer
SliM-LLM: Salience-Driven Mixed-Precision Quantization for Large Language Models
methods salience efficiency quantization language slim-llm llms
Prediction Models for Suicide Attempts among Adolescents Using Machine Learning Techniques
using data ml prediction 0 suicide adolescents
