Strength in Numbers: Growth of Canadian Clinician Investigator Training in the 21st Century
Bibliographic record
Abstract
PURPOSE: Enhancing clinician-investigator (CI) training at Canadian medical schools is urgently needed to bolster the dwindling work force of medical professionals carrying out patient-oriented research in a wide array of medical fields. The purpose of this study is to obtain, from the 15 Canadian medical schools that offer one or more CI training programs, data on the number of trainees, funding levels, attrition rates or other important metrics to evaluate the outcomes of such training efforts. METHODS: All Canadian CI programs were surveyed to collect demographic information for the academic year 2010-2011 and compared this to historical data collected by the Association of Faculties of Medicine of Canada (AFMC) and MD/PhD program funding data from the Canadian Institutes of Health Research (CIHR). RESULTS: Over the past decade, enrolment in Canadian CI training programs has increased approximately four-fold. Program-specific funding (CIHR) has also increased, but nearly 50% of MD/PhD trainees are still not supported through dedicated CIHR funding. CONCLUSION: It is too early to know to what extent this increase in both CI and funding will sustain the workforce of Canadian researchers carrying out patient-oriented research. Monitoring of CI training demographics across Canada, beyond this baseline study, will be essential to measure outcomes from CI training programs and to guide response from funding bodies and policy-makers.
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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 | 0.022 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 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".