An International Fellowship Training Program in Pediatric Emergency Medicine
Bibliographic record
Abstract
INTRODUCTION: The health care system reform in the People's Republic of China has brought plans for establishment of a universal coverage for basic health services, including services for children. This effort demands significant change in health care planning. Pediatric emergency medicine (PEM) is not currently identified as a specialty in China, and emergency medicine systems suffer from lack of appropriate training.In 2006, the Centre for International Child Health and the Department of Pediatrics, British Columbia Children's Hospital, Vancouver, Canada, initiated a fellowship training program in PEM for pediatricians working in emergency departments or critical care settings with the Children's Hospital of Fudan University, China. The main objective was to upgrade the professional and clinical experience of emergency physicians practicing PEM and build PEM capacity throughout China by training the future trainers. METHODS: After selecting trainees, the program included a structured curriculum over 2 years of training in China by Canadian and Australian PEM faculty and then practical exposure to PEM in Canada. All trainees underwent a structured evaluation after their final rotation in Canada. RESULTS: A total of 12 trainees completed the first 2 program cycles. The trainees considered the "overall rating of the training experience" as "excellent" (10/12) or "good" (2/12). All trainees considered the program as a relevant training to their practice and felt it will change their practice. They reported the program to be effective, with excellent complexity of content. DISCUSSION: Despite its current success, the program faces challenges in the development of the new subspecialty and ensuring its acceptance among other health care providers and decision makers. Identification and preparation of a capable training force to lead educational activities in China are daunting tasks. Time constraints, funding, and language barriers are other challenges. Future effort should be focused on improving and sustaining resuscitation capacity and enhancing triage systems.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.002 |
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".