A Clinician-Researcher Training Program for Family Medicine Residents
Bibliographic record
Abstract
PURPOSE: Research training for clinicians is becoming relatively common for postdoctoral trainees in academic institutions. In contrast, there are relatively few such training programs for family physician residents. The purpose of this article is to describe a novel program for family medicine trainees in Maccabi Health Services, a large Israeli health fund. METHODS: Following organizational approval and budget allocation, a call for family residents resulted in 18 applications, 15 of whom were selected for a two-year research training program. Each trainee submitted a research proposal, dealing with a community- based research question. Each protocol was allocated a budget. The Program, overseen by a steering committee of family physicians and scientists, has a designated clinical epidemiologist who coordinates all activities. The Project runs monthly face-to-face meetings where trainees present their research proposals. The group reviewed the protocols ahead of time, commented on them and criticized them. In parallel, the trainees participate in a detailed discussion of their research proposals face-to-face with the program director and clinical epidemiologist, and the revised research proposal is submitted to the Institution Review Board. RESULTS: The Program received enthusiastic responses from the trainees and from Maccabi Health Services, which has already approved the budget for the second year of the Program with a new stream of trainees. The approved research proposals dealt with original and important community-based clinical questions. CONCLUSIONS: With the aim of developing clinician-researchers in the field of family medicine, this novel program will help change the research climate in a large organization, where community-based family practitioners were not typically involved in 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 | 0.017 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.051 | 0.013 |
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".