Creation of a national emergency medicine enhanced competency directory for residency training
Bibliographic record
Abstract
Canadian emergency medicine Royal College residency training allows for pursuing extra training in enhanced competency areas. A wealth of enhanced competency training opportunities exist nationally. However, the search for the right fit is a challenging one because there is no centralized resource that catalogues all of these opportunities. A working group of the Canadian Association of Emergency Physicians (CAEP) Resident Section was assembled in 2016 to create a freely accessible and comprehensive directory of Canadian enhanced competency areas. The working group used stakeholder surveys (of residents, recent graduates, and faculty members), social media engagement, and program website searches. Information was collated into the first edition of a national enhanced competency directory, which is available at no cost at http://caep.ca/sites/caep.ca/files/enhancedcompdoc.pdf. Limitations include the scope defined by the working group and survey responses. A biannual update is also incorporated into the CAEP Resident Section portfolio to ensure it remains up-to-date.
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 | 0.013 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.035 | 0.012 |
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".