Canaries in the Mineshaft: The Dermatology Workforce Shortage in Eastern Ontario
Bibliographic record
Abstract
BACKGROUND: The number of dermatology residency positions in Canada has not reflected the growing workforce shortage. Until 2005, all dermatology residents at the University of Ottawa were committed to return to their funding area at the completion of their training. This has left Eastern Ontario with a critical shortage of dermatologists. OBJECTIVE: To survey dermatologists practicing in Eastern Ontario to understand the basis of the workforce shortage and outlook for the future. METHODS: Mailed surveys sent in 1999, 2003, and 2006 to all dermatologists in Eastern Ontario requesting demographic information, workload data, and future career plans. RESULTS: There was a 100% response rate in each survey year. Between 1999 and 2006, the total number of practicing dermatologists decreased from 26 to 23, whereas the average age increased from 51.4 to 57.4 years. The waiting time to see new and returning patients increased, from 5.8 to 18.5 weeks and 4.9 to 11.8 weeks, respectively. Ten of the 23 dermatologists practicing in 2006 plan to retire within the next 5 years. CONCLUSIONS: The inadequate supply of dermatologists in Eastern Ontario will increasingly threaten patient care. This emphasizes the need for additional funding for dermatology training positions, continued mentorship, and improved dermatology training for nondermatologists.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".