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STFM Provides Resources and Training to Current and Future Residency Learning Network Leaders

Announcing the 2024 Graduates

September 23, 2024—A second cohort of current and future residency learning network leaders completed training at the STFM Conference on Practice and Quality Improvement. The 21 participants, selected through an open call for applications, completed a full-day preconference workshop, plus conference sessions relevant to residency learning networks.

Sessions within the workshop covered topics such as:

  • Defining the “Common Purpose” of a Learning Network
  • Identifying a Backbone Organization
  • Governance and Management Structures
  • Communication Strategies
  • Managing Network Data
  • Structuring Meetings to Advance Objectives
  • Sustainability, Including Funding

The leadership training is one component of STFM's project to help family medicine residency programs meet new ACGME expectations for participation in residency learning networks. The project is funded by the ABFM Foundation.

Graduates:

  • Angela Cherry, MD, MBA, West Virginia University Rural Family Medicine Residency
  • Barbara Miller, MD, KCU-GMEC/Freeman Program
  • Chelsea Kolodziej, DO, Marion General Health Family Medicine Residency
  • Emilio Russo, MD, LSU Rural Family Medicine Bogalusa
  • James Chris Rule, LCSW, Baptist Health - UAMS Family Medicine Residency
  • Jason Lanham, MD, MA, Medical College of GA at Augusta Univ
  • Jennifer Svarverud, DO, University of Wisconsin Madison
  • Kathleen Young, PhD, Novant Health New Hanover Family Medicine Residency
  • Kathy Pabst, MBA, Missouri Academy of Family Physicians
  • Kelvin Wynn, MD, UICOMP Family Medicine Residency at Carle Health Methodist
  • Lauren Anderson, PhD, Rush-Esperanza Family Medicine Residency
  • Lauren Gibson-Oliver, MD, University of Arkansas for Medical Sciences Little Rock
  • Meggan Robinson, DO, Ascension Genesys Family Medicine Residency
  • Melissa Stephens MD, MS, East Central Mississippi Health Network Inc.
  • Michelle Keating, DO, Wake Forest University School of Medicine Family Medicine Residency- Atrium Health Wake Forest Baptist
  • Roger Garvin, MD, Oregon Health and Science University.
  • Soumya Sridhar, MBBS, MSc, University of Rochester-Highland Family Medicine Residency Program
  • Tana Chongsuwat, MD, McGaw Northwestern University
  • Victor Pulido, DO, Marian Regional Medical Center
  • William Bowen, MD, Medicos de El Centro
  • Zaiba Jetpuri, DO, UT Southwestern Family & Community Medicine

Faculty:

  • Corey Lyon DO, University of Colorado School of Medicine
  • Stephen Wilson MD, MPH, Boston University
  • Gretchen Irwin MD, MPH, University of Kansas School of Medicine –Wichita
  • Jay Fetter, MSHA, American Board of Family Medicine

On-Site Coaches:

  • Gretchen Irwin, MD, University of Kansas School of Medicine-Wichita
  • Tonya L Caylor, MD, University of Washington Network of Family Medicine Residencies
  • Jay Fetter, MHA, American Board of Family Medicine Foundation
  • Grace Shih, MD MAS, University of Washington Family Medicine Residency Program, WWAMI Family Medicine Residency Network

Administrative Leadership:

  • Mary Theobald, Society of Teachers of Family Medicine

 

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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:
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Try: "Can you help me update our Family Medicine clerkship curriculum?"
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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?"
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Clear Chat History When Switching Topics:

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Why this is important: Resetting ensures the AI does not carry over irrelevant information, which could lead to confusion or inaccurate answers.

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

Example:
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Try: "In the context of Family Medicine education, what are the best practices for integrating clinical simulations into the curriculum?"
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Break Down Complex Queries: If you have multiple questions, ask them separately.

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Try: Start with "What are the faculty development requirements for Family Medicine educators?" Then follow up with your other questions after receiving the response.
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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.

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