Changes in the Practice Patterns and Demographics of Ontario Dermatologists
Bibliographic record
Abstract
BACKGROUND: Changes in the practice patterns and demographics of Canadian dermatologists remain largely unknown and would be helpful in assessing the future practice of dermatology across Canada. OBJECTIVE: To assess changes in the population of Ontario dermatologists over time and the factors that influence their practice patterns, caseload, and the procedures they perform. METHODS: A retrospective population-based analysis was performed using comprehensive administrative data on Ontario Health Insurance Plan insured dermatology visits and procedures from April 1, 2009, to March 31, 2015. RESULTS: The number of dermatologists practicing in Ontario per 100 000 people increased from 1.52 (2009) to 1.62 (2014). During this period, the proportion of female dermatologists increased from 40% to 47%, and the proportion of male dermatologists decreased from 60% to 53%. The mean number of patient visits per dermatologist decreased from 6323 (2009) to 5877 (2014). Females saw a decrease from 4818 to 4181 visits, and males remained constant at 7274 to 7265 visits. Middle career dermatologists had more patient visits compared to those in their early or late career. A rural practice was associated with more patient visits compared to an urban one. The proportion of dermatologists providing nonemergency in-hospital patient services declined. The number of biopsies and malignant excisions performed increased. CONCLUSIONS: The number of dermatologists at the population level increased and the number of patient visits per dermatologist decreased. Career stage, physician sex, and practice location all affect the practice of dermatology. Future studies to assess underlying factors for these observations would be of value.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".