Mental health of computing professionals and students: A systematic literature review

• URL: http://arxiv.org/abs/2405.03416v1
• Date: Mon, 6 May 2024 12:31:34 GMT
• Title: Mental health of computing professionals and students: A systematic literature review
• Authors: Alicia Julia Wilson Takaoka, Kshitij Sharma,
• Abstract summary: We evaluate the state-of-the-art of research in mental health and well-being interventions, assessments, and concerns like anxiety and depression in computer science and computing education.
• Score: 2.532202013576547
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: The intersections of mental health and computing education is under-examined. In this systematic literature review, we evaluate the state-of-the-art of research in mental health and well-being interventions, assessments, and concerns like anxiety and depression in computer science and computing education. The studies evaluated occurred across the computing education pipeline from introductory to PhD courses and found some commonalities contributing to high reporting of anxiety and depression in those studied. In addition, interventions that were designed to address mental health topics often revolved around self-guidance. Based on our review of the literature, we recommend increasing sample sizes and focusing on the design and development of tools and interventions specifically designed for computing professionals and students.

Related papers

• Measuring the Mental Health of Content Reviewers, a Systematic Review [50.06646946044604]
  Many workers report long-term, potentially irreversible psychological harm. This work is similar to activities that cause psychological harm to other kinds of helping professionals even after small doses of exposure. This systematic review summarizes psychological measures from other professions and relates them to the experiences of content reviewers.
  arXiv  Detail & Related papers  (2025-02-01T00:50:15Z)
• The Transition from Centralized Machine Learning to Federated Learning for Mental Health in Education: A Survey of Current Methods and Future Directions [7.147592454527916]
  We propose a roadmap for integrating Federated Learning into mental health data analysis within educational settings. We provide an overview of mental health issues among students and review existing studies where ML has been applied to address these challenges.
  arXiv  Detail & Related papers  (2025-01-20T19:54:51Z)
• Understanding Student Sentiment on Mental Health Support in Colleges Using Large Language Models [5.3204794327005205]
  This paper uses public Student Voice Survey data to analyze student sentiments on mental health support with large language models (LLMs) The investigation of both traditional machine learning methods and state-of-the-art LLMs showed the best performance of GPT-3.5 and BERT on this new dataset.
  arXiv  Detail & Related papers  (2024-11-18T02:53:15Z)
• Explainable AI for Mental Disorder Detection via Social Media: A survey and outlook [0.7689629183085726]
  We conduct a thorough survey to explore the intersection of data science, artificial intelligence, and mental healthcare. A significant portion of the population actively engages in online social media platforms, creating a vast repository of personal data. The paper navigates through traditional diagnostic methods, state-of-the-art data- and AI-driven research studies, and the emergence of explainable AI (XAI) models for mental healthcare.
  arXiv  Detail & Related papers  (2024-06-10T02:51:16Z)
• Measuring Non-Typical Emotions for Mental Health: A Survey of Computational Approaches [57.486040830365646]
  Stress and depression impact the engagement in daily tasks, highlighting the need to understand their interplay. This survey is the first to simultaneously explore computational methods for analyzing stress, depression, and engagement.
  arXiv  Detail & Related papers  (2024-03-09T11:16:09Z)
• An Integrative Survey on Mental Health Conversational Agents to Bridge Computer Science and Medical Perspectives [7.564560899044939]
  We conduct a comprehensive literature review using the PRISMA framework, reviewing 534 papers published in both computer science and medicine. Our systematic review reveals 136 key papers on building mental health-related conversational agents with diverse characteristics of modeling and experimental design techniques.
  arXiv  Detail & Related papers  (2023-10-25T21:37:57Z)
• Empowering Psychotherapy with Large Language Models: Cognitive Distortion Detection through Diagnosis of Thought Prompting [82.64015366154884]
  We study the task of cognitive distortion detection and propose the Diagnosis of Thought (DoT) prompting. DoT performs diagnosis on the patient's speech via three stages: subjectivity assessment to separate the facts and the thoughts; contrastive reasoning to elicit the reasoning processes supporting and contradicting the thoughts; and schema analysis to summarize the cognition schemas. Experiments demonstrate that DoT obtains significant improvements over ChatGPT for cognitive distortion detection, while generating high-quality rationales approved by human experts.
  arXiv  Detail & Related papers  (2023-10-11T02:47:21Z)
• Handwriting and Drawing for Depression Detection: A Preliminary Study [53.11777541341063]
  Short-term covid effects on mental health were a significant increase in anxiety and depressive symptoms. The aim of this study is to use a new tool, the online handwriting and drawing analysis, to discriminate between healthy individuals and depressed patients.
  arXiv  Detail & Related papers  (2023-02-05T22:33:49Z)
• Intelligent interactive technologies for mental health and well-being [70.1586005070678]
  The paper critically analyzes existing solutions with the outlooks for their future. In particular, we:. give an overview of the technology for mental health,. critically analyze the technology against the proposed criteria, and. provide the design outlooks for these technologies.
  arXiv  Detail & Related papers  (2021-05-11T19:04:21Z)
• Psychometrics in Behavioral Software Engineering: A Methodological Introduction with Guidelines [19.40714760075466]
  We provide an introduction to psychometric theory for the evaluation of measurement instruments for software engineering researchers. We detail activities used when operationalizing new psychological constructs, such as item pooling, item review, pilot testing, item analysis, factor analysis, statistical property of items, reliability, validity, and fairness in testing and test bias. We hope to encourage a culture change in SE research towards the adoption of established methods from psychology.
  arXiv  Detail & Related papers  (2020-05-20T11:03:46Z)
• A Review of Computational Approaches for Evaluation of Rehabilitation Exercises [58.720142291102135]
  This paper reviews computational approaches for evaluating patient performance in rehabilitation programs using motion capture systems. The reviewed computational methods for exercise evaluation are grouped into three main categories: discrete movement score, rule-based, and template-based approaches.
  arXiv  Detail & Related papers  (2020-02-29T22:18:56Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.