LO15: Not a hobby anymore: Establishment of the Global Health Emergency Medicine organization at the University of Toronto to facilitate academic careers in global health for faculty and residents
Bibliographic record
Abstract
Introduction/Innovation Concept: Demand for training in global health emergency medicine (EM) practice and education across Canada is high and increasing. For faculty with advanced global health EM training, EM departments have not traditionally recognized global health as an academic niche warranting support. To address these unmet needs, expert faculty at the University of Toronto (UT) established the Global Health Emergency Medicine (GHEM) organization to provide both quality training opportunities for residents and an academic home for faculty in the field of global health EM. Methods: Six faculty with training and experience in global health EM founded GHEM in 2010 at a UT teaching hospital, supported by the leadership of the ED chief and head of the Divisions of EM. This initial critical mass of faculty formed a governing body, seed funding was granted from the affiliated hospital practice plan and a five-year strategic academic plan was developed. Curriculum, Tool, or Material: GHEM has flourished at UT with growing membership and increasing academic outputs. Five governing members and 9 general faculty members currently run 18 projects engaging over 60 faculty and residents. Formal partnerships have been developed with institutions in Ethiopia, Congo and Malawi, supported by five granting agencies. Fifteen publications have been authored to date with multiple additional manuscripts currently in review. Nineteen FRCP and CCFP-EM residents have been mentored in global health clinical practice, research and education. Finally, GHEM’s activities have become a leading recruitment tool for both EM postgraduate training programs and the EM department. Conclusion: GHEM is the first academic EM organization in Canada to meet the ever-growing demand for quality global health EM training and to harness and support existing expertise among faculty. The productivity from this collaborative framework has established global health EM at UT as a relevant and sustainable academic career. GHEM serves as a model for other faculty and institutions looking to move global health EM practice from the realm of ‘hobby’ to recognized academic endeavor, with proven academic benefits conferring to faculty, trainees and the institution.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.085 | 0.027 |
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".