⚡ Quick Summary
A recent study developed a virtual workshop aimed at educating psychiatry and psychology trainees on the use of artificial intelligence (AI) for suicide prevention research. The workshop significantly enhanced participants’ confidence in their natural language processing (NLP) knowledge and coding abilities.
🔍 Key Details
- 👩🎓 Participants: 10 trainees including residents, postdoctoral researchers, and graduate students
- 🖥️ Workshop Duration: 3 hours
- 📚 Topics Covered: Data characterization, data standardization, concept extraction, statistical analysis
- 💻 Technology Used: Python coding in Jupyter notebooks within Microsoft Azure Databricks
🔑 Key Takeaways
- 📊 AI education is crucial for mental health professionals to effectively implement AI tools in practice.
- 💡 The workshop focused on applying NLP techniques specifically for suicide prevention research.
- 📈 Participants’ confidence in NLP knowledge increased significantly from 1.35 to 2.79 (P=.002).
- 🛠️ Coding confidence also improved, rising from 1.33 to 2.25 (P=.01).
- 🔍 Feedback indicated a desire for future workshops to include thematic analysis and diverse datasets.
- 🌐 Future workshops will explore additional topics like large language models and multifaceted outcomes.
📚 Background
The integration of artificial intelligence in behavioral health sciences is becoming increasingly important. However, the lack of training opportunities for mental health professionals poses a significant barrier to the adoption of AI in clinical settings. This study highlights the necessity of AI education for trainees, enabling them to collaborate effectively with data scientists and develop essential computing skills.
🗒️ Study
Conducted as part of the Penn Innovation in Suicide Prevention Implementation Research Center, this study involved the development and evaluation of a virtual workshop designed to educate psychiatry and psychology trainees on the application of AI in suicide prevention research. The workshop utilized natural language processing (NLP) concepts and Python coding skills, providing hands-on experience in a secure cloud computing environment.
📈 Results
The workshop was attended by 10 trainees who reported a mean satisfaction score of 3.17 on a scale of 1-4. The significant increase in confidence regarding NLP knowledge and coding abilities demonstrates the workshop’s effectiveness in enhancing participants’ skills, with confidence levels rising notably after the training.
🌍 Impact and Implications
This study underscores the importance of tailored data science education for mental health professionals. By equipping trainees with the necessary skills to utilize AI and NLP techniques, we can improve research outcomes in suicide prevention and potentially other areas of mental health. The implications of this training extend beyond individual confidence, fostering a new generation of interdisciplinary researchers capable of leveraging technology in clinical practice.
🔮 Conclusion
The findings from this study highlight the transformative potential of AI education in psychiatry and psychology. By enhancing trainees’ confidence and skills in data science, we pave the way for more effective research and clinical applications in mental health. Future workshops promise to build on this foundation, exploring advanced topics and further enriching the educational experience for mental health professionals.
💬 Your comments
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Data Science Education for Residents, Researchers, and Students in Psychiatry and Psychology: Program Development and Evaluation Study.
Abstract
BACKGROUND: The use of artificial intelligence (AI) to analyze health care data has become common in behavioral health sciences. However, the lack of training opportunities for mental health professionals limits clinicians’ ability to adopt AI in clinical settings. AI education is essential for trainees, equipping them with the literacy needed to implement AI tools in practice, collaborate effectively with data scientists, and develop skills as interdisciplinary researchers with computing skills.
OBJECTIVE: As part of the Penn Innovation in Suicide Prevention Implementation Research Center, we developed, implemented, and evaluated a virtual workshop to educate psychiatry and psychology trainees on using AI for suicide prevention research.
METHODS: The workshop introduced trainees to natural language processing (NLP) concepts and Python coding skills using Jupyter notebooks within a secure Microsoft Azure Databricks cloud computing and analytics environment. We designed a 3-hour workshop that covered 4 key NLP topics: data characterization, data standardization, concept extraction, and statistical analysis. To demonstrate real-world applications, we processed chief complaints from electronic health records to compare the prevalence of suicide-related encounters across populations by race, ethnicity, and age. Training materials were developed based on standard NLP techniques and domain-specific tasks, such as preprocessing psychiatry-related acronyms. Two researchers drafted and demonstrated the code, incorporating feedback from the Methods Core of the Innovation in Suicide Prevention Implementation Research to refine the materials. To evaluate the effectiveness of the workshop, we used the Kirkpatrick program evaluation model, focusing on participants’ reactions (level 1) and learning outcomes (level 2). Confidence changes in knowledge and skills before and after the workshop were assessed using paired t tests, and open-ended questions were included to gather feedback for future improvements.
RESULTS: A total of 10 trainees participated in the workshop virtually, including residents, postdoctoral researchers, and graduate students from the psychiatry and psychology departments. The participants found the workshop helpful (mean 3.17 on a scale of 1-4, SD 0.41). Their overall confidence in NLP knowledge significantly increased (P=.002) from 1.35 (SD 0.47) to 2.79 (SD 0.46). Confidence in coding abilities also improved significantly (P=.01), increasing from 1.33 (SD 0.60) to 2.25 (SD 0.42). Open-ended feedback suggested incorporating thematic analysis and exploring additional datasets for future workshops.
CONCLUSIONS: This study illustrates the effectiveness of a tailored data science workshop for trainees in psychiatry and psychology, focusing on applying NLP techniques for suicide prevention research. The workshop significantly enhanced participants’ confidence in conducting data science research. Future workshops will cover additional topics of interest, such as working with large language models, thematic analysis, diverse datasets, and multifaceted outcomes. This includes examining how participants’ learning impacts their practice and research, as well as assessing knowledge and skills beyond self-reported confidence through methods such as case studies for deeper insights.
Author: [‘Donnelly HK’, ‘Mandell D’, ‘Hwang S’, ‘Schriver E’, ‘Vurgun U’, ‘Neill G’, ‘Patel E’, ‘Reilly ME’, ‘Steinberg M’, ‘Calloway A’, ‘Gallop R’, ‘Oquendo MA’, ‘Brown GK’, ‘Mowery DL’]
Journal: JMIR Med Educ
Citation: Donnelly HK, et al. Data Science Education for Residents, Researchers, and Students in Psychiatry and Psychology: Program Development and Evaluation Study. Data Science Education for Residents, Researchers, and Students in Psychiatry and Psychology: Program Development and Evaluation Study. 2026; 12:e75125. doi: 10.2196/75125