Pediatric Dermatology in Canada: A Broad Review of Population Needs, Workforce and Training With Proposed Solutions
Bibliographic record
Abstract
INTRODUCTION: The need for pediatric dermatology services is increasing across Canada. In parallel, the complexity of treatment with novel targeted therapeutics has increased. Currently, there is no accredited and limited non-accredited fellowship training access to pediatric dermatology in Canada. HYPOTHESIS: Understanding the current state of pediatric dermatology training in Canada will provide insight into opportunities for strategic improvement. METHODS: A survey was distributed to 44 pediatric dermatology providers. In addition, a review of the burden of pediatric skin disease and education/training in Canada was performed. RESULTS: Thirty-four specialists responded to the survey (77% response rate). One third of current pediatric dermatology providers are over 50 years old and half of these (15%) plan to retire within the next 5 years. Half of respondents were dermatologists, 35% were pediatricians, and 11% were double boarded. Almost all respondents practiced in an academic setting (94%). Most had further fellowship training in pediatric dermatology (82.4%) but only 57% achieved this training in Canada, due to lack of accredited or non-accredited funded fellowship positions. CONCLUSION: There is a high and growing need for pediatric dermatology specialty care in a diverse range of settings. The current provider population and training programs are insufficient to meet current and future demands. We highlighted solutions to close this gap between supply and demand including increased double board certification in Pediatrics and Dermatology, a protected pediatric stream within existing Dermatology residency training programs and accredited fellowships in Pediatric Dermatology for both dermatologists and pediatricians.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.017 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".