Barriers to Digital Mental Health Services among College Students

• URL: http://arxiv.org/abs/2507.21093v1
• Date: Mon, 30 Jun 2025 21:27:27 GMT
• Title: Barriers to Digital Mental Health Services among College Students
• Authors: Ha Na Cho, Kyuha Jung, Daniel Eisenberg, Cheryl A. King, Kai Zheng,
• Abstract summary: This study explores barriers to utilization of digital mental health Intervention (DMHI) among college students.<n>We identified nine key barriers to DMHI adoption and the use of in-person mental health services.
• Score: 5.680225451894852
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This qualitative study explores barriers to utilization of digital mental health Intervention (DMHI) among college students. Data are from a large randomized clinical trial of an intervention, eBridge, that used motivational interviewing for online counseling to connect students with mental health issues to professional services. We applied thematic analysis to analyze the feedback from the student participants regarding their experience of using the DMHI platform. We identified nine key barriers to DMHI adoption and the use of in-person mental health services: emotional distress, time constraints, privacy concerns, resource accessibility, financial challenges, medication stigma, dissatisfaction with communication, content clarity, and treatment-related concerns. Our findings emphasize the need for personalized, culturally sensitive interventions and improved strategies to enhance the access and engagement in mental health support for young adults.

Related papers

• Designing AI Tools for Clinical Care Teams to Support Serious Illness Conversations with Older Adults in the Emergency Department [53.52248484568777]
  The work contributes empirical understanding of ED-based serious illness conversations and provides design considerations for AI in high-stakes clinical environments.<n>We conducted interviews with two domain experts and nine ED clinical care team members.<n>We characterized a four-phase serious illness conversation workflow (identification, preparation, conduction, documentation) and identified key needs and challenges at each stage.<n>We present design guidelines for AI tools supporting SIC that fit within existing clinical practices.
  arXiv  Detail & Related papers  (2025-05-30T21:15:57Z)
• MentalChat16K: A Benchmark Dataset for Conversational Mental Health Assistance [13.373260490163709]
  MentalChat16K is an English benchmark dataset combining a synthetic mental health counseling dataset and a dataset of anonymized transcripts.<n>This curated dataset is designed to facilitate the development and evaluation of large language models for conversational mental health assistance.<n>The dataset prioritizes patient privacy, ethical considerations, and responsible data usage.
  arXiv  Detail & Related papers  (2025-03-13T20:25:10Z)
• Exploring Socio-Cultural Challenges and Opportunities in Designing Mental Health Chatbots for Adolescents in India [5.511657284487823]
  Mental health challenges among Indian adolescents are shaped by unique cultural and systemic barriers.<n>This study explores how adolescents perceive mental health challenges and interact with digital tools.
  arXiv  Detail & Related papers  (2025-03-11T15:52:05Z)
• Towards Privacy-aware Mental Health AI Models: Advances, Challenges, and Opportunities [61.633126163190724]
  Mental illness is a widespread and debilitating condition with substantial societal and personal costs.<n>Recent advances in Artificial Intelligence (AI) hold great potential for recognizing and addressing conditions such as depression, anxiety disorder, bipolar disorder, schizophrenia, and post-traumatic stress disorder.<n>Privacy concerns, including the risk of sensitive data leakage from datasets and trained models, remain a critical barrier to deploying these AI systems in real-world clinical settings.
  arXiv  Detail & Related papers  (2025-02-01T15:10:02Z)
• Digital Health Innovations for Screening and Mitigating Mental Health Impacts of Adverse Childhood Experiences: Narrative Review [0.4194295877935868]
  This study presents a narrative review of the use of digital health technologies (DHTs) and artificial intelligence to screen and mitigate risks and mental health consequences associated with ACEs among children and youth.<n>The use of DHTs, machine learning tools, natural learning processing, and artificial intelligence can positively help in mitigating ACEs and associated risk factors.
  arXiv  Detail & Related papers  (2025-01-30T21:22:54Z)
• 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)<n>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)
• Enhancing AI-Driven Psychological Consultation: Layered Prompts with Large Language Models [44.99833362998488]
  We explore the use of large language models (LLMs) like GPT-4 to augment psychological consultation services. Our approach introduces a novel layered prompting system that dynamically adapts to user input. We also develop empathy-driven and scenario-based prompts to enhance the LLM's emotional intelligence.
  arXiv  Detail & Related papers  (2024-08-29T05:47:14Z)
• MindfulDiary: Harnessing Large Language Model to Support Psychiatric Patients' Journaling [16.929899228710852]
  We present MindfulDiary, a mobile journaling app incorporating an Large Language Model (LLMs) to help psychiatric patients document daily experiences through conversation. We found that MindfulDiary supported patients in consistently enriching their daily records and helped psychiatrists better empathize with their patients through an understanding of their thoughts and daily contexts.
  arXiv  Detail & Related papers  (2023-10-08T17:00:04Z)
• MentalHealthAI: Utilizing Personal Health Device Data to Optimize Psychiatry Treatment [1.696974372855528]
  Mental health disorders remain a significant challenge in modern healthcare, with diagnosis and treatment often relying on subjective patient descriptions and past medical history. We propose a personalized mental health tracking and mood prediction system that utilizes patient physiological data collected through personal health devices.
  arXiv  Detail & Related papers  (2023-07-09T22:30:47Z)
• 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)
• Mental Illness Classification on Social Media Texts using Deep Learning and Transfer Learning [55.653944436488786]
  According to the World health organization (WHO), approximately 450 million people are affected. Mental illnesses, such as depression, anxiety, bipolar disorder, ADHD, and PTSD. This study analyzes unstructured user data on Reddit platform and classifies five common mental illnesses: depression, anxiety, bipolar disorder, ADHD, and PTSD.
  arXiv  Detail & Related papers  (2022-07-03T11:33:52Z)
• MET: Multimodal Perception of Engagement for Telehealth [52.54282887530756]
  We present MET, a learning-based algorithm for perceiving a human's level of engagement from videos. We release a new dataset, MEDICA, for mental health patient engagement detection.
  arXiv  Detail & Related papers  (2020-11-17T15:18:38Z)

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.