Practitioner Perspectives on the Uses of Generative AI Chatbots in Mental Health Care: Mixed Methods Study
Bibliographic record
Abstract
Background: Generative artificial intelligence (AI) chatbots have the potential to improve mental health care for practitioners and clients. Evidence demonstrates that AI chatbots can assist with tasks such as documentation, research, counseling, and therapeutic exercises. However, research examining practitioners' perspectives is limited. Objective: This mixed-methods study investigates: (1) practitioners' perspectives on different uses of generative AI chatbots; (2) their likelihood of recommending chatbots to clients; and (3) whether recommendation likelihood increases after viewing a demonstration. Methods: Participants were 23 mental health practitioners, including 17 females and 6 males, with a mean age of 39.39 (SD 16.20) years. In 45-minute interviews, participants selected their 3 most helpful uses of chatbots from 11 options and rated their likelihood of recommending chatbots to clients on a Likert scale before and after an 11-minute chatbot demonstration. Results: Binomial tests found that Generating case notes was selected at greater-than-chance levels ( 15/23, 65%; P=.001), while Support with session planning (P=.86) and Identifying and suggesting literature (P=.10) were not. Although 55% (12/23) were likely to recommend chatbots to clients, a binomial test found no significant difference from the 50% threshold (P=.74). A paired samples t test found that recommendation likelihood increased significantly (19/23, 83%; P=.002) from predemonstration to postdemonstration. Conclusions: Findings suggest practitioners favor administrative uses of generative AI and are more likely to recommend chatbots to clients after exposure. This study highlights a need for practitioner education and guidelines to support safe and effective AI integration in mental health care.
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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 | 0.110 | 0.115 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".