Graduates of northern Ontario family medicine residency programs practise where they train.
Bibliographic record
Abstract
OBJECTIVE: To examine where graduates of the Northeastern Ontario Family Medicine (NOFM) residency program in Sudbury and the Family Medicine North (FMN) program in Thunder Bay practise after graduation, using cross-sectional and longitudinal analyses. METHODS: Data from the Scott's Medical Database were examined. All physicians who graduated from NOFM and FMN between 1993 and 2002 were included in this analysis. Differences in the location of first practice between NOFM and FMN graduates were tested using chi-squared tests. Logistic regression analyses were used to examine the impact of the training program on a physician's first, as well as continuing, practice location. RESULTS: Between 1993 and 2002, FMN graduates were 4.56 times more likely (95% confidence interval [CI] 2.34-8.90) to practise in rural areas, compared with NOFM graduates, but NOFM graduates were 2.50 times more likely than FMN graduates (95% CI 1.35-4.76) to practise in northern Ontario. There was no statistically significant difference between the graduates of the 2 programs in the likelihood of working in either northern Ontario or a rural area. About two-thirds (67.5%) of all person-years of medical practice provided by NOFM and FMN graduates took place in northern Ontario or rural areas outside the north. CONCLUSION: NOFM and FMN have been successful in producing family physicians to work in northern Ontario and rural areas. Results from this study add to the growing evidence from Canada and abroad that rural or northern medical education and training increases the likelihood that the graduates will practise in rural or northern communities.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".