If You Build It They Will Come…and Stay: A Community-Based Family Medicine Program
Bibliographic record
Abstract
INTRODUCTION: In response to a government request to address physician shortages in underserved communities, the University of Toronto (U of T) established the Family Medicine Residency Program (FMRP) at the Royal Victoria Regional Health Centre (RVH) in Barrie, Ontario, Canada. Prior to establishing the FMRP, approximately 21% of Barrie residents did not have a family physician. This study investigated residents' training experiences, strengths and opportunities for improvement of a community FMRP, reasons why graduates choose to work in Barrie after graduation, and graduates' practice setting and location. METHODS: RVH graduates from 2011-2016 (N=45) were invited to participate. Semistructured one-on-one interviews sought insight into graduates' experience in the program. We collected online survey data to gather demographic information. We determined current practice location using a government-funded data set and the public registry of the provincial licensing body. RESULTS: Analysis of qualitative data provided insights into an overwhelmingly positive educational experience that contributed to graduates choosing to stay and work in Barrie. Participants noted the wide range of hands-on training opportunities as a strength of the program. They perceived that the program added value to the local community by increasing capacity to provide care to an underserved patient population. Tracking data demonstrated that two-thirds of graduates continued to work in the RVH region after graduation. CONCLUSIONS: The successful establishment of a new university-affiliated FMRP in an underserved community provides a strong mechanism to recruit physicians. Training in this setting provided excellent educational experiences to residents, who felt prepared to enter independent practice upon completion of training.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".