Creation and implementation of the Ottawa Handbook of Emergency Medicine
Bibliographic record
Abstract
Implication Statement Medical students face multiple academic challenges during their transition to clerkship, including the ability to navigate various educational resources and translate acquired knowledge clinically. The Ottawa Handbook of Emergency Medicine (EM) was created by referencing EM textbooks and relevant literature, followed by a local peer-review process. A website metrics assessment was performed to assess student uptake. Implementation of the Ottawa Handbook of EM across Canadian clerkship curriculums is anticipated to bridge the EM knowledge gap for junior learners. Énoncé des implications de la recherche Les étudiants en médecine sont confrontés à de multiples défis académiques au moment de leur transition vers l'externat, notamment à celui de se servir de diverses ressources éducatives et d'appliquer leurs connaissances dans un contexte clinique. Le Guide d'Ottawa de médecine d'urgence (MU) a été élaboré à partir de manuels de MU et de la littérature pertinente, et il a fait l'objet d'un processus local d'examen par les pairs. Une évaluation bibliométrique a été effectuée pour évaluer son utilisation par les étudiants. L'application du Guide d'Ottawa de médecine d'urgence dans le cadre des cursus canadiens d'externat devrait permettre de combler les lacunes qu'auraient les étudiants débutants en matière de médecine d'urgence.
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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 | 0.064 | 0.138 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.007 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 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".