Division of Experimental Medicine, McGill University, Montreal, Canada.
Center for Outcome Research and Evaluation (CORE), McGill University Health Centre Research Institute, Montréal, Quebec, Canada.
Qual Life Res. 2023 Feb;32(2):413-424. doi: 10.1007/s11136-022-03245-5. Epub 2022 Sep 10.
To estimate among people living with chronic HIV, to what extent providing feedback on their health outcomes will affect the number and specificity of patient-formulated self-management goals.
A personalized feedback profile was produced for individuals enrolled in a Canadian HIV Brain Health Now study. Goal specificity was measured by total number of specific words (matched to a domain-specific developed lexicon) per person-words using text mining techniques.
Of 176 participants enrolled and randomly assigned to feedback and control groups, 110 responses were received. The average number of goals was similar for both groups (3.7 vs 3.9). The number of specific words used in the goals formulated by the feedback and control group were 642 and 739, respectively. Specific nouns and actionable verbs were present to some extent and "measurable" and "time-bound" words were mainly missing. Negative binomial regression showed no difference in goal specificity among groups (RR = 0.93, 95% CI 0.78-1.10). Goals set by both groups overlapped in 8 areas and had little difference in rank.
Personalized feedback profile did not help with formulation of high-quality goals. Text mining has the potential to help with difficulties of goal evaluation outside of the face-to-face setting. With more data and use of learning models automated answers could be generated to provide a more dynamic platform.
在患有慢性 HIV 的人群中,评估提供健康结果反馈在多大程度上会影响患者制定自我管理目标的数量和针对性。
为参加加拿大 HIV 大脑健康现在研究的个体生成个性化反馈档案。目标针对性通过使用文本挖掘技术,针对每个人的特定词汇(与特定领域开发的词汇表匹配)来衡量特定词汇的总数。
在纳入并随机分配到反馈组和对照组的 176 名参与者中,收到了 110 份回复。两组的平均目标数量相似(3.7 对 3.9)。反馈组和对照组制定的目标中使用的特定词数量分别为 642 个和 739 个。在一定程度上存在特定名词和可操作动词,但主要缺少“可衡量”和“有时限”的词。负二项回归显示,组间目标针对性无差异(RR=0.93,95%CI 0.78-1.10)。两组设定的目标在 8 个领域重叠,排名差异较小。
个性化反馈档案无助于制定高质量的目标。文本挖掘具有帮助解决面对面设置之外的目标评估困难的潜力。随着更多数据和使用学习模型,自动化回答可以生成,从而提供更具动态性的平台。