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Yes We Can . . . and Did!

Deborah Taylor, PhD, BFEF Codirector, Central Maine Medical Center FMR, Lewiston, Maine

The Group on Behavioral Science (members totaling more than 300) has had an exciting and productive year (2009–2010). Our group’s 2009–2010 goal was to serve as the home for all residency and medical school family medicine educators who aspire to teach a biopsychosocial (including family, community, culture, and spiritual dimensions) model of health care. All of our measurable objectives were designed to move us closer to that BHAG (Big Hairy Audacious Goal, that is). The most rewarding outcome was the development of an STFM Behavioral Science/Family Systems Educator Fellowship (BFEF), in collaboration with the leadership of the Group on the Family in Family Medicine and the Medical College of Wisconsin Department of Family Medicine, sponsor of the Forum for Behavioral Science in Family Medicine. The design of this fellowship focuses on educational tracks within the STFM Annual Spring Conference and the Forum for Behavioral Science in Family Medicine, exposure to curriculum resource materials, and mentorship/support through a small-group format to faculty with newly defined roles (5 years or less experience) around teaching the behavioral science curriculum in family medicine residencies.

The BFEF Steering Committee included the elected leadership of the two STFM groups, the director of the Forum for Behavioral Science in Family Medicine, and a meta-mentor for our small-group mentors—all played a pivotal role in selecting fellows (26 applicants for 12 positions in Year 1), developing conference tracks and resource materials, making small-group assignments including the recruitment of small-group mentors, and establishing methods of measurement of efficacy and value. Year 1 of the fellowship was launched at the 2010 STFM Annual Spring Conference in Vancouver. There was a tremendous amount of excitement and enthusiasm among the first fellowship class, the “riper” educators who are serving as the inaugural small-group mentors, and the STFM leadership and general attendees at the conference. Many asked “How did you pull this off?” There is actually a fairly simple answer to this complex question—relationships, passion, cooperation, and collaboration. We so appreciate the support of the STFM Board of Directors and staff for believing in a new vision. At the risk of sounding like an Oscar acceptance speech, it is with great joy, appreciation, and fondness that I also get to publicly thank those who made this fellowship a reality:

BFEF Steering Committee: Victoria Gorski, MD, Group on Family cochair and Fellowship codirector; Dennis Butler, PhD, Forum for Behavioral Science Planning Committee chair; Bill Gunn, PhD, Group on Family cochair; Laurel Milberg, PhD, meta mentor; Amy Romain, MSW, Group on Behavioral Science cochair; and Julie Schirmer, MSW, Group on Behavioral Science cochair.

2010–2011 BFEF Small-group Mentors: John Cavacece, MD; Mike Floyd, EdD; Kate Neely, MD; Nancy Newman, MD; Valerie Ross, MSW; and Mary Talen, PhD.

This Fellowship has been approved by the STFM Board of Directors for a second fellowship class—call for applications will take place in the fall 2010.

"Never doubt that a small group of thoughtful, committed people
can change the world. Indeed, it is the only thing that ever has."

—Margaret Mead

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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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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.

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