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STFM Members Win Various 2024 Awards

STFM Members Recognized at NAPCRG Annual Meeting

Several members of the Society of Teachers of Family Medicine (STFM) received awards at the 2024 North American Primary Care Research Group (NAPCRG) Annual Meeting in Québec City, Canada. The conference was held November 20–24.

STFM members who received awards were:

  • Irf Asif, MD, University of Alabama at Birmingham
  • Perry Dickinson, MD, University of Colorado Anschutz
  • Warren Newton, MD, MPH, president and CEO of the American Board of Family Medicine
  • Amanda Weidner, MPH, University of Illinois Chicago

Dr Dickinson received the NAPCRG Maurice Wood Award for Lifetime Contribution to Primary Care Research. This annual award honors a researcher who has made outstanding contributions to primary care research. Dr Dickinson is a past president of both STFM and NAPCRG.

Dr Asif, Dr Newton, and Dr Weidner each received the NAPCRG President’s Award.

NAPCRG supports and nurtures clinicians, scientists, students, and patients in pursuit of primary care research. Donate to NAPCRG online to help expand the reach of primary care research.

Irf Asif, MD (University of Alabama at Birmingham) Perry Dickinson, MD (University of Colorado Anschutz) Warren Newton, MD, MPH (president and CEO of the American Board of Family Medicine)  Amanda Weidner, MPH (University of Illinois Chicago)

Bich-May Nguyen, MD, MPH, Receives the Texas Academy of Family Physicians Diversity and Health Equity Leadership Award

STFM member Bich-May Nguyen, MD, MPH, University of Houston, has been awarded the Texas Academy of Family Physicians (TAFP) Diversity and Health Equity Leadership Award. She received the award during TAFP’s Annual Session and Primary Care Summit in The Woodlands on Nov. 9, 2024.

The award honors physicians who advocate for health equity, encourage increased representation from groups historically underrepresented in medicine, or promote inclusivity in health care settings and organized medicine. Read the press release from TAFP about Dr Nguyen’s award.

Dr Nguyen has been a STFM member since 2012 and is a current member of PRiMER journal. She served on the STFM Communications Committee from 2017–2021 and was a past STFM Medical Editing Fellow.

Bich-May Nguyen, MD, MPH (University of Houston)

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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.

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