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Dyads Selected for STFM Antiracism Learning Collaborative

December 13, 2021—The STFM Antiracism Task Force received a  robust response to a call for applications for an Academic Family Medicine Antiracism Learning Collaborative. Twenty dyads were selected from 57 applications. Dyads include a faculty member from a family medicine department or residency program and an ally from the same program, institution, or health system. 

Selected dyads:

  • Jonisha Brown, MD, and Keyona Oni, MD, Atrium Health Family Medicine Residency Program
  • Angela Echiverri, MD, MPH, and Kaitlin HollandBerry, MD, Contra Costa Family Medicine Residency Program
  • Bari Laskow, MD, and Sudha Subramanyam, MD,  Family Medicine Residency of Idaho—Nampa
  • Theresa Nevarez, MD, MBA, and Fathima Sarah Nazarkhan, MD, Harbor UCLA Family Medicine Residency
  • Nicholas Shungu, MD, MPH, and Sean Haley, MD, MPH, Medical University of South Carolina
  • Tanya White-Davis, PsyD, and  Ellen Tattelman, MD, Montefiore Medical Center Residency Program in Family and Social Medicine
  • Ricardo Hood, MD, and Adora Otiji, MD, Ross University School of Medicine
  • Nicole Gordon MD, and Jerry Garcia, PhD, SeaMar Marysville Family Medicine Residency Program
  • Rynita Bohler, MD, and Anna Goroncy, MD, MEd, The Christ Hospital/University of Cincinnati Family Medicine Residency Program
  • Krys Foster, MD, MPH, and Danielle Snyderman, MD, CMD, Thomas Jefferson University Depart of Family and Community Medicine Residency Program
  • Emily Trambert-Kylstra, MD, MPH, and Kimberley R. Nichols, MD, UNC School of Medicine
  • Lisa Harris, DO, and Christina Kelly, MD, Uniformed Services University of the Health Sciences
  • Charles Vega MD, and Ursula Worsham, PhD, University of California, Irvine
  • Randy Jackson Jr., MD, and Kirsten Day, MD, University of California, San Francisco
  • David Dwayne Henderson, MD, and Gian Grant-McGarvey, MD, University of Connecticut School of Medicine
  • Alesia Jones, PhD, and Manorama Khare, PhD, MS, University of Illinois College of Medicine Rockford Family Medicine Residency Program
  • Colleen Loo-Gross, MD, MPH, and Samuel Ofei-Dodoo, PhD, MPA, MA, University of Kansas School of Medicine—Wichita
  • Eduardo Medina MD, MPH, and Andrea Westby, MD, University of Minnesota Department of Family Medicine and Community Health
  • Didi Ebert, DO, MPH, MS, and Melva Landrum, University of North Texas Health Science Center—Texas College of Osteopathic Medicine
  • Tiffany Ho, MD, MPH, and Laura Elizabeth Moreno, MD, University of Utah Department of Family & Preventive Medicine

The Academic Family Medicine Learning Collaborative is an IRB-approved study (approval pending) to measure the effectiveness of training and implementation of various projects and strategies to:

  • Empower and educate participants so they will identify racist structures and behaviors within their academic institutions and become leaders for change
  • Promote allyship
  • Spread effective change strategies

Between January 2022 and September 2023, selected pairs will attend two full-day in-person sessions and five virtual sessions and will work on projects to reduce racism within their institutions. Pairs will be assigned mentors who will provide guidance and expertise.

This project is supported by a grant from Adtalem Global Education Foundation.

 

 

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AI Chatbot Tips

Tips for Using STFM's AI Assistant

STFM's AI Assistant is designed to help you find information and answers about Family Medicine education. While it's a powerful tool, getting the best results depends on how you phrase your questions. Here's how to make the most of your interactions:

1. Avoid Ambiguous Language

Be Clear and Specific: Use precise terms and avoid vague words like "it" or "that" without clear references.

Example:
Instead of: "Can you help me with that?"
Try: "Can you help me update our Family Medicine clerkship curriculum?"
Why this is important: Ambiguous language can confuse the AI, leading to irrelevant or unclear responses. Clear references help the chatbot understand exactly what you're asking.

2. Use Specific Terms

Identify the Subject Clearly: Clearly state the subject or area you need information about.

Example:
Instead of: "What resources does STFM provide?"
Try: "I'm a new program coordinator for a Family Medicine clerkship. What STFM resources are available to help me design or update clerkship curricula?"
Why this is better: Providing details about your role ("program coordinator") and your goal ("design or update clerkship curricula") gives the chatbot enough context to offer more targeted information.

3. Don't Assume the AI Knows Everything

Provide Necessary Details:The STFM AI Assistant has been trained on STFM's business and resources. The AI can only use the information you provide or that it has been trained on.

Example:
Instead of: "How can I improve my program?"
Try: "As a program coordinator for a Family Medicine clerkship, what resources does STFM provide to help me improve student engagement and learning outcomes?"
Why this is important: Including relevant details helps the AI understand your specific situation, leading to more accurate and useful responses.

4. Reset if You Change Topics

Clear Chat History When Switching Topics:

If you move to a completely new topic and the chatbot doesn't recognize the change, click the Clear Chat History button and restate your question.
Note: Clearing your chat history removes all previous context from the chatbot's memory.
Why this is important: Resetting ensures the AI does not carry over irrelevant information, which could lead to confusion or inaccurate answers.

5. Provide Enough Context

Include Background Information: The more context you provide, the better the chatbot can understand and respond to your question.

Example:
Instead of: "What are the best practices?"
Try: "In the context of Family Medicine education, what are the best practices for integrating clinical simulations into the curriculum?"
Why this is important: Specific goals, constraints, or preferences allow the AI to tailor its responses to your unique needs.

6. Ask One Question at a Time

Break Down Complex Queries: If you have multiple questions, ask them separately.

Example:
Instead of: "What are the requirements for faculty development, how do I register for conferences, and what grants are available?"
Try: Start with "What are the faculty development requirements for Family Medicine educators?" Then follow up with your other questions after receiving the response.
Why this is important: This approach ensures each question gets full attention and a complete answer.

Examples of Good vs. Bad Prompts

Bad Prompt

"What type of membership is best for me?"

Why it's bad: The AI Chat Assistant has no information about your background or needs.

Good Prompt

"I'm the chair of the Department of Family Medicine at a major university, and I plan to retire next year. I'd like to stay involved with Family Medicine education. What type of membership is best for me?"

Why it's good: The AI Chat Assistant knows your role, your future plans, and your interest in staying involved, enabling it to provide more relevant advice.

Double Check Important Information

While the AI Chat Assistant is a helpful tool, it can still produce inaccurate or incomplete responses. Always verify critical information with reliable sources or colleagues before taking action.

Technical Limitations

The Chat Assistant:

  • Cannot access external websites or open links
  • Cannot process or view images
  • Cannot make changes to STFM systems or process transactions
  • Cannot access real-time information (like your STFM Member Profile information)

STFM AI Assistant
Disclaimer: The STFM Assistant can make mistakes. Check important information.