Collaborating to Achieve the Optimal Family Medicine Workforce
Bibliographic record
Abstract
When the Family Medicine for America's Health (FMAHealth) Workforce Education and Development Tactic Team (WEDTT) began its work in December 2014, one of its charges from the FMAHealth Board was to increase family physician production to achieve the diverse primary care workforce the United States needs. The WEDTT created a multilevel interfunctional team to work on this priority initiative that included a focus on student, resident, and early-career physician involvement and leadership development. One major outcome was the adoption of a shared aim, known as 25 x 2030. Through a collaboration of the WEDTT and the eight leading family medicine sponsoring organizations, the 25 x 2030 aim is to increase the percentage of US allopathic and osteopathic medical students choosing family medicine from 12% to 25% by the year 2030. The WEDTT developed a package of change ideas based on its theory of what will drive the achievement of 25 x 2030, which led to specific projects completed by the WEDTT and key collaborators. The WEDTT offered recommendations for the future based on its 3-year effort, including policy efforts to improve the social accountability of US medical schools, strategy centered around younger generations' desires rather than past experiences, active involvement by students and residents, engagement of early-career physicians as role models, focus on simultaneously building and diversifying the family medicine workforce, and security of the scope future family physicians want to practice. The 25 x 2030 initiative, carried forward by the family medicine organizations, will use collective impact to adopt a truly collaborative approach toward achieving this much needed goal for family medicine.
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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 | 0.026 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.003 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.002 | 0.019 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.013 | 0.005 |
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".