应用数据科学实践,从他们的网站上识别中学后自闭症支持计划的特征.
Michael West1, Matthew Tincani2, Donald Hantula2
1Temple University, Philadelphia, PA, USA. michael.west0004@temple.edu.
Journal of autism and developmental disorders
|October 13, 2025
概括
一种新的数据科学方法有效地识别了美国大学的自闭症支持计划 (ASP). 虽然有希望,但许多机构缺乏这些计划,并且对自闭症学生的诊断和成本等进入障碍仍然存在.
科学领域:
- 高等教育 高等教育
- 自闭症谱系障碍 自闭症谱系障碍
- 残疾人支持服务服务.
背景情况:
- 越来越多的自闭症学生进入中学后教育.
- 一些机构建立自闭症支持计划 (ASP).
- 缺乏关于ASP,其服务和访问要求的全面,最新信息.
研究的目的:
- 引入一种新的数据科学方法来识别美国中学后自闭症支持计划 (ASP).
- 描述已识别的ASP,包括招生,成本,结构和支持服务.
- 为满足对ASP更新和全面数据的需求.
主要方法:
- 利用已建立的数据科学技术来识别ASP.
- 在以前的研究方法上进行了扩展.
- 能够识别ASP的特点:招生,成本,结构和支持.
主要成果:
- 开发的方法比以前的方法更有效和更强大.
- 确定了49个以前未被目录的ASP.
- 确定ASP的详细特征,包括申请流程,共同支持和程序成本.
结论:
- ASP的领域是动态的,需要自动化工具来更新数据.
- 绝大多数的高等教育机构都不提供ASP.
- 进入ASP的障碍包括强制性ASD诊断和相关的财务成本.
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