Impact of the Number of Dermatologists on Dermatology Biomedical Research: A Canadian Study
Bibliographic record
Abstract
BACKGROUND: Fewer dermatologists than other clinical specialists are entering and being retained as physicians in the Canadian medical workforce. Studies suggest that dermatologist numbers may influence skin disease outcomes. No study has questioned whether the number of clinical dermatologists can influence academic productivity. OBJECTIVE: To quantify the correlation of the number of dermatologists with biomedical scientific production in this field from 1996 to 2008 in Canada. METHODS: Canadian dermatology biomedical scientific production from SCImago Journal & Country Rank (SJR) were merged with Canadian Medical Association (CMA) dermatologist demographic data. Linear regression analyses were used to model the relationships. RESULTS: The low growth of dermatologist numbers by 8.16% in Canada from 1996 to 2008 correlates with a small increase in articles by 7.59% published in this subject area during this period. This has reduced the scientific importance of Canadian dermatology in the world. CONCLUSION: The number of dermatologists was a significant predictor of biomedical research production in the field of dermatology. This suggests that specialist availability may be one factor influencing dermatology research and publications.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.013 |
| 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) | 0.006 | 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".