探索成人助听器用户的体验:含义提取方法,内容模式,以及与人口统计和结果变量相关的关联
Ilze Oosthuizen1,2, De Wet Swanepoel1,2,3, Ryan L Boyd4
1Department of Speech-language Pathology and Audiology, University of Pretoria, South Africa.
International journal of audiology
|October 26, 2024
概括
自动化语言分析确定了助听器用户体验的六个关键因素,包括生活变化和社会情况. 这些因素与整体满意度和自我报告的听力困难有显著的相关性.
科学领域:
- 听力学 听力学是指听力学.
- 自然语言处理自然语言处理.
- 数据科学数据科学数据科学
背景情况:
- 了解助听器用户的体验对于提高设备效率和患者满意度至关重要.
- 开放文本响应提供了丰富的定性数据,但需要先进的分析方法来提取有意义的见解.
研究的目的:
- 应用自动化语言分析技术,特别是含义提取方法 (MEM) 和主要组件分析 (PCA),以确定影响助听器体验的关键因素.
- 探索这些已识别的因素与助听器整体满意度,以及自我报告的听力困难,一般健康和身体活动之间的关系.
主要方法:
- 采用了一个探索性,横截面的研究设计,使用一个在线问卷.
- 用MEM,PCA,回归和相关性分析对538名成年助听器用户的样本进行了分析.
主要成果:
- 从MEM分析中出现了六个关键因素:生活变化,社会情况,生活质量,影响和语音理解,沟通和互动,以及音乐和环境声音.
- IOI-HA (利益解释) 的因素3显示了与PCA因素的最显著的相关性. 因素1 (生活变化) 和2 (社会状况) 显著预测了IOI-HA总得分.
- 在自我报告的听力困难和因素1,4和5之间发现了积极的相关性. 第1因子也与一般健康相关,第2因子与身体活动相关.
结论:
- 自动化自然语言分析为助听器用户的多面体验提供了宝贵的见解.
- 鉴定出来的因素为理解用户体验提供了一个框架,对助听器的安装和康复有潜在的影响.
- 建议进一步完善这些方法,以提高未来研究中的临床相关性和通用性.
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