Mystakidou Kyriaki, Tsilika Eleni, Parpa Efi, Katsouda Emmanuela, Sakkas Palvos, Galanos Antonis, Vlahos Lambros
Department of Radiology, Areteion Hospital, School of Medicine, University of Athens, Vas. Sofias 76, 115 28 Athens, Greece.
Psychooncology. 2006 Sep;15(9):828-33. doi: 10.1002/pon.1029.
Preparatory grief encompasses grief for losses that have already occurred, are currently being experienced, and losses that will or might ensue in the future after the death, as a consequence of it.
To examine the relative contribution of demographic and clinical variables in predicting cancer patients' preparatory grief as recorded from the Preparatory Grief in Advanced Cancer Patients (PGAC) scale. Moreover, researchers were interested in determining whether these dimensions were independently and uniquely associated with preparatory grief.
Two hundred advanced cancer patients treated in a Pain Relief and Palliative Care Unit completed the PGAC scale, while researchers recorded data on demographic characteristics, disease status and treatment regimen.
The analyses showed that the most significant correlations were found between preparatory grief and age (r = -0.227, p = 0.001), gender (p = 0.006), family status (p = 0.019), performance status (p = 0.010), surgery (p = 0.029), opioids (p = 0.019), and diagnosis (p = 0.038). In the prediction of preparatory grief, the contribution of age, performance status, history of other surgery, gender and opioids is high.
Awareness of the specific patients' demographic and medical characteristics, such as old age, poor performance status, history of other surgery, female gender, and strong opioids, contribute to the prediction of patients' preparatory grief.
预备性悲伤包括对已经发生、正在经历的损失,以及在死亡后将会或可能随之而来的损失的悲伤。
研究人口统计学和临床变量在预测癌症患者预备性悲伤方面的相对贡献,该预备性悲伤通过晚期癌症患者预备性悲伤(PGAC)量表进行记录。此外,研究人员还想确定这些维度是否与预备性悲伤独立且独特相关。
在疼痛缓解与姑息治疗科接受治疗的200名晚期癌症患者完成了PGAC量表,同时研究人员记录了人口统计学特征、疾病状态和治疗方案的数据。
分析表明,预备性悲伤与年龄(r = -0.227,p = 0.001)、性别(p = 0.006)、家庭状况(p = 0.019)、体能状态(p = 0.010)、手术(p = 0.029)、阿片类药物(p = 0.019)和诊断(p = 0.038)之间存在最显著的相关性。在预测预备性悲伤方面,年龄、体能状态、其他手术史、性别和阿片类药物的贡献较大。
了解特定患者的人口统计学和医学特征,如老年、体能状态差、其他手术史、女性性别和强效阿片类药物,有助于预测患者的预备性悲伤。