The current state of general paediatric fellowships in Canada
Bibliographic record
Abstract
INTRODUCTION: The field of Paediatric Medicine has grown tremendously over the last two decades. Several niche areas of practice have emerged, and opportunities for focused training in these areas have grown in parallel. The landscape of 'General Paediatric Fellowship' (GPF) Programs in Canada is not well described; this knowledge is needed to promote standardization and high-quality training across Canada. This study explores the structure and components of existing GPFs in Canada and identifies the interest and barriers to providing such programs. METHODS: A questionnaire was created to explore the landscape of GPF Programs in Canada. Invitations to participate were sent to leaders of General Paediatric Divisions across Canada, with a request to forward the survey to the most appropriate individual to respond within their local context. RESULTS: A total of 19 responses (95%) representing 17 different Canadian universities were obtained. Eight universities offered a total of 13 GPF Programs in 2019, with one additional university planning to start a program in the coming year. Existing programs were variable in size, structure and curriculum. Most programs identified as Academic Paediatric Programs, with an overlap in content and structure between Academic Paediatrics and Paediatric Hospital Medicine programs. The majority of respondents felt there was a need for GPF Programs in Canada but cited funding as the most common perceived barrier. CONCLUSION: A growing number of GPF Programs exist in Canada. Current fellowship programs are variable in structure and content. Collaboration between programs is required to advance GPF training in Canada.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".