The number, scope and geographic distribution of clinical researchers in Canada
Bibliographic record
Abstract
PURPOSE: Despite international concerns about declining numbers of clinical researchers, the number of clinical researchers in Canada remains undocumented. METHODS: The number and geographic distribution of clinical researchers in Canada and the scope of their research activities were estimated using, as an indicator, the data on clinical research projects funded by the Canadian Institutes for Health Research (CIHR). RESULTS: Between fiscal years 1999-00 and 2006-07, 1,041 individual researchers--approximately 130 per year--were principal investigators (PIs) on clinical research grants. One hundred and 26 researchers received salary awards; 449 supervisors oversaw the clinical research activities of 230 fellows and 223 students with trainee awards. An additional 2,305 individuals served only as co-investigators on grants. Most (863 [83%]) PIs received funding for operating grants; 196 (19%) PIs received funding for randomized controlled trials. The institute of neurosciences, mental health and addiction funded the highest number of researchers (187 [18%] PIs, 40 [17%] fellows, and 73 [33%] students). Among provinces, Quebec led the nation with the highest number (45) of PIs per million population. Ontario had the highest number of clinical research fellows (10 per million population) while Quebec and Saskatchewan each hosted more students (11 per million). CONCLUSION: The number of Canadian investigators with funding for clinical research from CIHR was low. Although the ideal ratio of clinical to basic research capacity is not known, the possibility that the gap between laboratory-based research and clinical research is larger in Canada than in the United States is discussed.
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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 | 0.009 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.012 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".