A Framework for Designing, Implementing, and Sustaining a National Simulation Network
Bibliographic record
Abstract
The use of networks for sharing and distributing information, for institutional collaboration, and action programs is commonplace. In 1989, the Medical Council of Canada began the implementation of a national clinical licensing examination to assess physicians for practice skills and decision making using standardized or simulated patients in an Objective Structured Clinical Examination format. Once fully implemented, the examination was administered through a network of medical schools at 16 locations across Canada in two languages twice yearly. That network has functioned successfully for 17 years. This article reviews the literature and examines the reasons and incentives for the long-term sustainability of the network. Based on that assessment, a framework is presented for analyzing, designing, and sustaining a national simulation network. It emphasizes the need for an iterative approach and identifies the success factors that can facilitate the adoption of a national simulation network for use in professional credentialing and licensure.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.048 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.010 | 0.018 |
| Scholarly communication | 0.016 | 0.012 |
| Open science | 0.006 | 0.010 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".