实践应用行为分析的治疗师对机器学习的看法:一项全国调查
Tam Doan1, Brittany Sullivan1, Jeana Koerber2
1Western Michigan University Homer Stryker M.D. School of Medicine, Kalamazoo, MI USA.
Behavior analysis in practice
|January 10, 2025
概括
应用行为分析 (ABA) 治疗师对机器学习 (ML) 对数据收集感兴趣,但缺乏熟悉. 熟悉ML增加了舒适度,而经验降低了对其行为识别准确性的信心.
科学领域:
- 行为科学 行为科学
- 人工智能的人工智能
- 临床心理学 临床心理学
背景情况:
- 应用行为分析 (ABA) 治疗自闭症谱系障碍 (ASD) 的实时数据收集具有挑战性,特别是在攻击性等快速行为方面.
- 关于使用机器学习 (ML) 来自动化ABA数据收集的研究有限.
研究的目的:
- 调查ABA治疗师对使用ML在治疗期间自动收集数据的看法.
- 确定影响治疗师对ABA实践ML技术的信心,舒适度和信任的因素.
主要方法:
- 对ABA治疗师进行了一项全国性调查.
- 收集的数据包括对ML的熟悉程度,对ML准确性的信心,对ML使用的舒适性以及对数据安全的信心.
主要成果:
- 大多数ABA治疗师对ML不熟悉.
- 更熟悉ML的治疗师报告了更高的信心,舒适和对其应用的信任.
- 增加的ABA认证和经验与对ML准确识别行为能力的信心降低有关.
结论:
- ABA治疗师认为ML在数据收集方面具有潜在价值,但需要对其好处和安全性进行教育.
- 解决隐私问题和促进治疗师和计算机科学家之间的合作对于在ABA中成功实施ML至关重要.
- 未来的发展应该专注于提高ML的感知精度和实用性,以减少治疗师负担和改善客户干预.
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