114: A Curriculum in Pediatric Global Health: A New Competency-Based, Multi-Disciplinary Educational Program
Bibliographic record
Abstract
Global health teaching at our institution is overseen by the Global Child Health Program (GCHP) of the Department of Pediatrics. The GCHP is lead by a multidisciplinary steering committee formed by physicians, nurses, allied health professionals, and pediatric residents. In order to fulfill adequately its teaching mandate, the Program developed a new competency-based, multi-disciplinary educational program that would reflect the complexity of Global Child Health in 2014. Specific CanMEDS-based learning outcomes were created for each tier of the teaching program: The General Pediatric Global Health Curriculum which consists of mandatory sessions for all pediatric residents. The Advanced Curriculum in Pediatric Global Health, a two-year course with monthly teaching sessions offered to health professionals with a specific interest in this area of practice. Global Health Elective. A review of the literature was done using the terms “pediatric”, “global health” and “education''. 382 articles were identified and 24 were selected after abstract review by the principal investigator. No Canadian competency-based pediatric curricula were identified. Based on the current literature, curriculum topics were chosen and CanMEDs outcomes were created. These were reviewed by the education subcommittee of the Global Child Health Program and by a pediatrician specialized in medical education. Three sets of outcomes were created, one for each aspect of the teaching program. They were further divided to follow the seven categories of the CanMeds framework (Example Table 1.) The learning outcomes of the Global Child Health Program are, to our knowledge, the first multidisciplinary Canadian competency based outcomes to be created. It is an essential guide used in our institution to teach all pediatric residents the basics of Pediatric Global Health and to allow our multidisciplinary group of health professionals to develop advanced skills and expertise in this constantly expanding field.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".