Family Medicine Research in the United States From the late 1960s Into the Future.
Bibliographic record
Abstract
BACKGROUND: When the new field of family medicine research began a half century ago, multiple individuals and organizations emphasized that research was a key mission. Since the field's inception, there have been notable research successes for which family medicine organizations, researchers, and leaders-assisted by federal and state governments and private foundations-can take credit. Research is a requirement for family medicine residency programs but not individual residents, and multiple family medicine departments offer research training in various forms for learners at all levels, including research fellowships. Family physicians have developed practice-based research networks (PBRNs) to conduct investigations and generate new knowledge. The field of family medicine has seen the creation of new journals to support the publication of research relevant to practicing family physicians. Nonetheless, in spite of much growth and many successes, family physicians and their research have been underrepresented in research funding. Clinical presentations in family medicine are often complex, poorly-differentiated, and exist as one of several patient complaints and diagnoses, and are not well-covered by the narrow basic-science and specialty research that defines most of the biomedical research enterprise. Overall health in the United States would benefit from a more robust research participation and greater support for family medicine research.
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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 | 0.009 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".