Meaningful Partnerships: Stages of Development of a Patient and Family Advisory Council at a Family Medicine Residency Clinic
Bibliographic record
Abstract
BACKGROUND: Partnering with patients and families is a crucial step in optimizing health. A patient and family advisory council (PFAC) is a group of patients and family members working together collaboratively with providers and staff to improve health care. OBJECTIVE: This study aimed to describe the creation of a PFAC within a family medicine residency clinic. To understand the successful development of a PFAC, challenges, potential barriers, and positive outcomes of a meaningful partnership will be reported. METHODS: The stages of PFAC development include leadership team formation and initial training, PFAC member recruitment, and meeting launch. Following a description of each stage, outcomes are outlined and lessons learned are discussed. PFAC members completed an open-ended survey and participated in a focus group interview at the completion of the first year. Interviewees provided feedback regarding (1) favorite aspects or experiences, (2) PFAC impact on a family medicine clinic, and (3) future projects to improve care. Common themes will be presented. RESULTS: The composition of the PFAC consisted of 18 advisors, including 8 patient and family advisors, 4 staff advisors, 4 resident physician advisors, and 2 faculty physician advisors. The average meeting attendance was 12 members over 11 meetings in the span of the first year. A total of 13 out of 13 (100%) surveyed participants were satisfied with their experience serving on the PFAC. CONCLUSIONS: PFACs provide a platform for patient engagement and an opportunity to drive home key concepts around collaboration within a residency training program. A framework for the creation of a PFAC, along with lessons learned, can be utilized to advise other residency programs in developing and evaluating meaningful PFACs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.035 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".