Preliminary findings of assessing NOSM's social accountability toward Northern & rural Francophone communities
Bibliographic record
Abstract
The Northern Ontario School of Medicine (NOSM) was founded in 2003 with a specific \nmandate to improve the health of the people and communities of Northern Ontario. The objective \nof this project was to determine whether NOSM was remaining socially accountable to the \nhealthcare needs of Francophones in Northern Ontario by: (i) graduating physicians capable of \noffering French language services, and (ii) identifying whether these physicians are locating their \npractice in areas of greatest need for such services. Specifically, if NOSM learners’ who reported \nhaving French as a language of competence at entry to, and exit from, medical school located their \npractices in areas densely populated by Francophones in Northern Ontario. To do so, three data \nsources were utilized; a) the Centre for Rural and Northern Health Research Tracking Study data \nof NOSM learners; b) the College of Physicians and Surgeons of Ontario (CPSO) public “Find a \nDoctor” search; and c) the 2016 Statistics Canada Census data. Laurentian University Research \nEthics Board approval was obtained for this project. In this project, I found that 39% and 38% of \nlearners identified French competency in the entry and exit tracking survey respectively. Of the \nFrench respondents identified in the tracking surveys, 20% and 21% identified French competency \nin practice (CPSO registry). A total of 70% of NOSM French-competent graduates were currently \npractising in a Francophone community in Ontario (i.e., total population having ≥ 10% of \nFrancophones), with ~92% of French-competent respondents having located their practice in a \nFrench community in the north (having a postal code beginning with P). These preliminary results \nsuggest that NOSM is achieving its mission of social accountability towards Francophones in \nnorthern Ontario and more specifically, that NOSM is helping to improving the availability of \nFrench language medical services for Francophones in Ontario.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".