Rural training tracks in the United States for family physician training
Bibliographic record
Abstract
Introducao: Rural Training Tracks have recaptured momentum in the United States due to the shortage of training opportunities and physicians located in rural communities. Distributed medical education for both medical students and residents has proven successful and recent efforts have provided data and experience to increase rural medical education and access to care for rural patients. Objetivos: The RTT Collaborative has been established with the purpose of sustaining health professions education in rural places through mutual encouragement, peer learning, practice improvement, and the delivery of technical expertise in support of a quality rural workforce. Metodologia ou descricao da experiencia: Evidence relating to Rural Training Tracks in the United Staes is growing. Surveys and case studies nationally have led to an increased understanding of the structure of the programs themselves as well as the data related to graduate outcomes. This poster presentation will highlight descriptive research projects, efforts involving medical student recruitment, program support and new rural program development. Resultados: The number of Rural Training Tracts in the United States is now increasing. The structure of Rural Training Tracks is becoming better understood by analysis of curriculum, economic funding and community support. Knowledge is increasing for both the medical school and residency training. The RTT Collaborative is a new organization working with medical education programs, promoting student interest in rural training programs, maintaining a database of program demographics and outcomes, working with the relevant bodies to define and establish new means and standards of accreditation, and promoting excellence in rurally located community-embedded medical and health professions education. Conclusoes ou hipoteses: Rural Training Tracks are increasing in the United States in conjunction with an appreciation for how distributed medical education provides an increased access to rural family medicine training. A growing understanding regarding these programs and support for sustaining this effort is key to contributing to rural health human resources in a changing healthcare system in the United States.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 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".