Enhancing Connections-Palliative Care: A Cancer Parenting Program for Patients with Advanced Cancer and Their Children

项目概要/摘要 本研究的目的是检验癌症亲职教育的短期效果 一项针对被诊断患有不可治愈癌症的育儿父母的计划，名为“增强联系-姑息” 护理方案（EC-PC）。该计划包括完全脚本化的干预会议，将由 由经过专门培训的患者教育者进行电话咨询。EC-PC已经过中试测试，并已准备好发挥功效 试验.如果父母被诊断患有不可治愈的癌症[由TNM IV期确定]， 硬肿瘤的癌症或非实体瘤中2个或更多个治疗失败伴疾病复发的周期];读作， 写和说英语作为他们选择的语言之一;有一个5-17岁的孩子住在 至少有50%的时间与生病的父母和另一位没有生病的父母或父母代理人在一起;并有机会获得 电话.研究受试者将从4个研究中心招募：华盛顿大学/西雅图癌症中心 护理联盟;乔治敦大学/隆巴迪综合癌症中心;夏威夷大学/夏威夷 癌症中心和东南肿瘤学/南卡罗来纳州大学。完成后，签署通知 同意和基线测量，父母将被随机分配到实验或替代治疗对照组 组实验组每两周接受5次电话教育， 受过专门训练的耐心教育者。对照组将收到“我应该对孩子们说什么？"的邮件，一 为晚期癌症患者的父母提供指导，并由经过专门培训的患者教育者电话联系， 将使用脚本来指导家长如何从小册子中获得最多的信息。主要终点 将在基线后3个月使用线性混合模型评估疗效，目的是 治疗分析和符合方案分析。五个主要结果将评估疗效：父母的焦虑和 抑郁情绪;父母帮助孩子管理父母癌症死亡人数的技能;父母的自我， 与孩子谈论癌症相关问题的有效性;以及孩子的焦虑/抑郁 心情将在6个月和9个月时描述和评价变更的稳定性。估计效应量 在主要结局的.33-81中，该研究将有大于0.80的把握度来检测显著的 实验组和对照组之间的差异，p= 0.05（双尾）。 我们的分析样本将是492名确诊的父母和他们的492名非患病同居伴侣/父母 5-17岁儿童的代理人。除了测试功效外，拟议的研究还将测试理论上的 使用结构方程模型和验证性因素分析的干预模型;评估 干预对研究期间父母死亡儿童行为-情绪适应的影响 使用线性混合模型从非患病父母获得的数据;并检查 父母的症状负担和家庭环境对治疗结果的影响。

PROJECT SUMMARY/ABSTRACT The purpose of the proposed study is to test the short-term efficacy of a cancer parenting education program for child-rearing parents diagnosed with non-curable cancer, called Enhancing Connections-Palliative Care Program (EC-PC). The program consists of fully scripted intervention sessions that will be delivered by telephone by a specially trained patient educator. The EC-PC has been pilot tested and is ready for efficacy testing. Parents will be eligible if they are diagnosed with non-curable cancer [determined by TNM Stage IV cancer for hard tumors or 2 or more cycles of treatment failure with disease relapse in non-solid tumors]; read, write and speak English as one of their languages of choice; have a child between the ages of 5-17 living in the home at least 50% of the time with the ill parent and another non-ill parent or parent surrogate; and have access to a telephone. Study participants will be recruited from 4 study centers: University of Washington/ Seattle Cancer Care Alliance; Georgetown University/ Lombardi Comprehensive Cancer Center; University of Hawaii/ Hawai’i Cancer Center, and Southeast Oncology/University of South Carolina. After completing signed informed consent and baseline measures, parents will be randomized to an experimental or alternative treatment control group. The experimental group will receive 5 telephone-delivered educational sessions every 2 weeks by specially trained patient educators. The control group will be mailed “What Should I Say to the Children?”, a guide for parents with advanced cancer, and be phone contacted by a specially trained patient educator who will use a script to guide the parent on ways to gain the most information from the booklet. Primary endpoints for testing efficacy will be assessed at 3-months post-baseline using Linear Mixed Models using an intent to treat analysis and a per-protocol analysis. Five primary outcomes will assess efficacy: parents' anxiety and depressed mood; parents' skills in helping their child manage the toll of the parents' cancer; parents' self- efficacy in talking with their child about the child's cancer-related concerns; and the child's anxious/depressed mood. The stability of changes will be described and evaluated at 6- and 9-months. With estimated Effect sizes of .33-81 on the primary outcomes, the study will have greater than 0.80 power to detect a significant difference between the experimental and control group, p=.05 (2-tailed). Our sample for analysis will be 492 diagnosed parents and their 492 non-ill coinhabiting partners/parent surrogates of children ages 5-17. In addition to testing efficacy, the proposed study will test the theoretical model of the intervention using structural equation modeling with confirmatory factor analysis; evaluate the impact of the intervention on children’s behavioral-emotional adjustment whose parents die during the study using Linear Mixed Models on data obtained from the non-ill parent; and examine the moderating effects of parents' symptom burden and family environment on treatment outcomes using Linear Mixed Models.