Milestones on the social accountability journey
Bibliographic record
Abstract
Objective To assess the effect of different levels of exposure to the Northern Ontario School of Medicine’s (NOSM’s) distributed medical education programs in northern Ontario on FPs’ practice locations. Design Cross-sectional design using longitudinal survey and administrative data. Setting Canada. Participants All 131 Canadian medical graduates who completed FP training in 2011 to 2013 and who completed their undergraduate (UG) medical degree or postgraduate (PG) residency training or both at NOSM. Intervention Exposure to NOSM’s medical education program at the UG (n = 49) or PG (n = 31) level or both (n = 51). Main outcome measures Primary practice location in September of 2014. Results Approximately 16% (21 of 129) of FPs were practising in rural northern Ontario, 45% (58 of 129) in urban northern Ontario, and 5% (7 of 129) in rural southern Ontario. Logistic regression found that more rural Canadian background years predicted rural practice in northern Ontario or Ontario, with odds ratios of 1.16 and 1.12, respectively. Northern Canadian background, sex, marital status, and having children did not predict practice location. Completing both UG and PG training at NOSM predicted practising in rural and northern Ontario locations with odds ratios of 4.06 to 48.62. Conclusion Approximately 61% (79 of 129) of Canadian medical graduate FPs who complete at least some of their training at NOSM practise in northern Ontario. Slightly more than a quarter (21 of 79) of these FPs practise in rural northern Ontario. The FPs with more years of rural background or those with greater exposure to NOSM’s medical education programs had higher odds of practising in rural northern Ontario. This study shows that NOSM is on the road to reaching one of its social accountability milestones.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
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".