Yellowknife: Canada’s First Circumpolar Family Medicine Residency Site
Bibliographic record
Abstract
PROBLEM: Canada's Northwest Territories (NWT), like other regions in the circumpolar north primarily inhabited by Indigenous peoples, faces challenges in recruiting and retaining physicians. Communities in this vast, diverse region depend largely on external medical professionals for health care. Consequently, these communities receive discontinuous medical care from physicians who lack local knowledge and are available only temporarily. The shortage of physicians for people residing in northern Canada requires a sustainable, long-term solution. APPROACH: The authors describe establishing Canada's first circumpolar family medicine residency training site in Yellowknife, NWT. The site was launched in 2020 as a partnership between the University of Alberta, Alberta Health Services, and 3 local health authorities in the NWT. The residency site, which bases residents in the local community, is expected to positively impact family physician recruitment and retention by allowing residents to build connections with local communities and identify as a northern physician. OUTCOMES: As of fall 2022, 4 residents had trained with the Yellowknife family medicine residency site. Two of these 4 residents graduated in 2022, both of whom plan to continue practicing medicine in the NWT. Residents have positively influenced medical care in the NWT, providing care in close to 20 small and remote communities. The presence of residents decreased appointment wait-times for some teams by as much as 60%, improved primary care screening, and enabled the provision of medical services at critical times. Furthermore, their presence has fostered academic spirit in the medical communities and had a positive impact on the communities as a whole. NEXT STEPS: The authors provide key insights and lessons learned from the establishment of the remote residency site. To develop and improve the site, continuous program evaluation is planned.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.058 | 0.006 |
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".