The 2nd Annual Clinical Scientist Trainee Symposium, August 22, 2017, London, Canada
Bibliographic record
Abstract
Clinician scientists play a critical role in bridging research and clinical practice. Unfortunately, the neglect of research training in medical schools has created clinicians who are unable to translate evidence from literature to practice. Furthermore, the erosion of research training in medical education has resulted in clinicians who lack the skills required for successful scientific investigation. To counteract this, the Schulich School of Medicine & Dentistry has made an effort to engage trainees, at all levels, in the research process. The 2nd Annual Clinician Scientist Trainee Symposium was held in London, Ontario, Canada on August 22, 2017. Organized each year since 2016 by the Schulich Research Office, the symposium features research being conducted by trainees in Schulich's Clinical Research Training Program. The focus this year was on the current state of clinician-scientist training in Canada and visions for the path ahead.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.167 | 0.060 |
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".