The Canadian Dermatology Workforce Survey: Implications for the Future of Canadian Dermatology—Who will be <i>your</i> skin expert?
Bibliographic record
Abstract
OBJECTIVE: To survey Canadian dermatologists for specialty-specific physician resource information including demographics, workload and future career plans. BACKGROUND AND METHODS: In 2001, the Canadian Dermatology Association (CDA) surveyed 555 dermatologists in Canada to gain specialty-specific physician resource information. Three hundred and seventy-one dermatologists (69%) provided information about themselves, their workloads and their future career goals. RESULTS: The average Canadian dermatologist is 52 years old and 35% of practicing dermatologists are over the age of 55. Eighty-nine percent of dermatologists practice in an urban setting, 19% include practice in a rural setting while less than 0.5% practice in remote areas. Canadian dermatologists spend 61% of their clinical time providing services in Medical Dermatology. Within 5 years, 50% of dermatologists reported that they plan to reduce their practices or retire. CONCLUSION: The Canadian Dermatology Workforce Survey provides a snapshot of the current practice of dermatology in Canada. It also serves to highlight the critical shortage of dermatologists, which will continue to worsen without immediate, innovative planning for the future.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".