The Role of Chatbots in Mental Health Interventions: User Experiences
Bibliographic record
Abstract
This study aims to explore the user experiences of individuals interacting with mental health chatbots, identifying key themes related to engagement, efficacy, satisfaction, barriers, and potential improvements. The objective is to inform the design and implementation of chatbot interventions to better meet the needs of users seeking mental health support. A qualitative research design was employed, utilizing semi-structured interviews with 22 participants who have used mental health chatbots. Thematic analysis was conducted to extract major and minor themes from the transcribed interviews, focusing on the aspects of user interaction, perceived benefits, and areas for enhancement in chatbot functionality. Five major themes were identified: User Engagement and Interaction, Perceived Efficacy, User Satisfaction and Trust, Barriers and Limitations, and Future Directions and Improvements. These encompassed various categories such as ease of use, personalization, emotional support, privacy concerns, technological issues, and suggestions for advanced AI and better integration with professional care. Participants valued chatbots for their accessibility and personalized support but highlighted the need for improvements in emotional understanding and technical reliability. Mental health chatbots are a promising tool for supporting individuals with their mental health needs, offering benefits in terms of engagement, personalized support, and perceived efficacy. However, addressing identified barriers such as technological limitations and emotional disconnect is crucial for enhancing user satisfaction and trust. Future developments should focus on incorporating advanced AI technologies, ensuring user privacy, and integrating chatbots more closely with traditional mental health services.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".