多模态Web应用于青少年心理咨询师的情商推断

Prerna Agarwal, Anupama Ray, A. Shah, Akshay Gugnani, Priyanka Halli, Shubham Atreja, Gargi Dasgupta
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引用次数: 1

摘要

在印度,每10万人中只有0.3名精神科医生和0.047名心理学家,而像美国这样的国家,每10万人中有29名心理学家(根据世卫组织的数据),从而导致咨询服务和心理保健的缺乏。幸运的是,印度的研究人员发现,由非专业咨询师而不是专家提供的心理健康干预措施,在治疗和预防心理健康问题方面更有效。然而,从一堆候选人中选择一名外行顾问是一项非常重要但耗时且乏味的任务,因为我们在评估简历和标准面试中的情感能力、隐性偏见和促进技能方面存在缺陷。在本文中,我们提出了一个高度可扩展的网络应用程序,可以帮助招聘情商高的非专业咨询师。后端框架测量了几个重要的情商特征,这些特征对未来的非专业咨询师至关重要。该框架使用多模式数据,并提供潜在咨询师的排名。结果和推论有助于确定每种情态的重要性,并对识别情感技能的关键特征提供见解。我们将预测的排名与面试官(一名临床心理学家和一名精神科医生)给出的排名进行比较,并认识到该过程自动化的好处,以及对面试问题、歧视性特征和多模态评估的重要性进行更深入分析的必要性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Multimodal Web Application to Infer Emotional Intelligence of Adolescent Counsellor
There are only 0.3 psychiatrists and 0.047 psychologists per 100,000 people in India, compared to a country like the US, which has 29 psychologists per 100,000 people (according to WHO), thereby leading to lack of counselling services and mental health-care. Fortunately, researchers in India have found mental health interventions delivered by lay counsellors rather than specialists to be effective in treating and preventing mental health problems. However, choosing a lay counsellor from a pool of candidates becomes a very important but time-consuming and tedious task because of our deficits in evaluating emotional capabilities, implicit biases and facilitation skills in a resume and standard interview. In this paper, we present a highly scalable web application that can help in hiring emotionally intelligent lay-counselors. The backend framework measures several vital emotional intelligence features that are crucial in a prospective lay counsellor. The framework uses multi-modal data and provides a ranking of potential counsellors. The results and inferencing help establish the importance of each modality and gives insights on features that are key to identify the emotional skills. We compare the predicted rankings to those given by the interviewers (a clinical psychologist and a psychiatrist) and recognize the benefits of automation of the process as well as a need for a deeper analysis of interview questions, discriminative features and importance of multi-modality assessments.
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