Supporting Family Medicine Research Capacity: The Critical Role and Current Contributions of US Family Medicine Organizations
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Family medicine is continuously advanced by a reinforcing research enterprise. In the United States, each national family medicine organization contributes to the discipline's research foundations. We sought to map the unique and interorganizational roles of the eight US family medicine professional organizations participating in Family Medicine for America's Health (FMAHealth) in supporting family medicine research. METHODS: We interviewed leaders and reviewed supporting materials from organizations participating in FMAHealth. We explored existing activities, capacity, and collaboration. We identified areas of strength and opportunities for growth and synergy with respect to how the family of family medicine nurtures family medicine research. RESULTS: The FMAHealth organizations support certain aspects of the family medicine research infrastructure. Six domains were identified through this work: showcasing scholarship, communication and dissemination, workforce development, data-driven initiatives, performing primary research, and advocacy for family medicine research. Each organization's areas of emphasis differ, but we found substantial collaboration on initiatives across organizations, possibly attributable to the fact that many members belong to more than one organization. CONCLUSIONS: Deliberate contributions to each of the six domains identified herein will be important for the future success of family medicine research. Key opportunity areas described here include coordinated and strategic advocacy for increased funding for family medicine research, dedicated investment in training opportunities, protected effort to grow the next generation of family medicine researchers, pilot funding to build a research base for future high-impact research, and infrastructure to facilitate cross-institutional collaboration and data sharing.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.083 | 0.127 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.018 | 0.014 |
| Scholarly communication | 0.018 | 0.015 |
| Open science | 0.002 | 0.018 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".