Learners’ experiences of an enhanced surgical skills training program for family physicians
Bibliographic record
Abstract
BACKGROUND: Family Physicians with Enhanced Surgical Skills (FPESS) have sustained rural operative care, including local access to caesarean section, in many communities across rural Canada and internationally. The contemporary role of FPESS within the health system, however, has not been without challenges. The 12-month Prince Albert Enhanced Surgical Skills (ESS) program intakes two learners a year and is one of only two accredited programs in Canada offering a scope of surgical practice beyond operative delivery. METHODS: This paper highlights the results of an evaluation of graduates' experiences of training and the post-training environment. Graduates were practicing in Western and Northern Canada after completing the ESS training program, specifically in British Columbia, Alberta, Manitoba, and the Northwest Territories. RESULTS: Findings suggest the overall success of the program in meeting learners' needs. There was a close match between the training curriculum and post-training practice. CONCLUSION: The findings from the post training experience suggest that sustainability of ESS is linked to 1) creating pathways to privileges between the ESS community and the Health Authorities, 2) building functional and trusting relationships with surgical specialists, and 3) creating a web of accessible effective rurally appropriate surgical Continuing Professional Development (CDP). Ongoing CPD is identified as essential in increasing the comfort of FPESS.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| 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".