Surgical Task‐Sharing in the Western Canadian Arctic: A Networked Model Between Family Physicians with Enhanced Surgical Skills and Specialist Surgeons
Bibliographic record
Abstract
BACKGROUND: With the loss of generalism in the surgical specialties, there has been a move in Canada to train family physicians in enhanced surgical skills (FP-ESS) to address the surgical needs of rural and remote populations. This research project sought to describe one network integrating FP-ESS and specialist surgeons, focusing on the role of FP-ESS and their relationship with specialist surgeons, in the surgical care of the Beaufort Delta Region of the Northwest Territories of Canada. METHODS: Using a participatory approach, semi-structured interviews were conducted with 22 stakeholders within the surgical system. Interviews were transcribed and reviewed, then imported into NVivo 12 for analysis. First-level coding was performed based on both deductive and inductive reasoning in an iterative fashion during interview collection to develop and refine the codebook. This was followed by second-level categorizing. RESULTS: The FP-ESS physicians provide cesarean section services to maintain a local obstetrics program, to provide gastrointestinal endoscopy, and to provide emergency on-call support, as described by one stakeholder. FP-ESS work together with specialist surgeons through an informal network keeping surgical care as close to home as possible. FP-ESS within this health regions were seen as "a really big gain to the system." CONCLUSIONS: This study deepens our understanding of rural surgical service delivery, in particular where FP-ESS and specialist surgeons function collaboratively. It also contributes to strengthening rural surgical systems in Canada and therefore to addressing the health gap between rural/remote/indigenous and urban populations.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.038 | 0.014 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".