Bibliographic record
Abstract
Clinician investigators (CI) are a growing sector within the research community. Given the emphasis on patient-oriented research, the need for more physicians with the aptitude to conduct translational research has never been greater. Despite this, there is limited literature on the current Canadian CI training programs. The Clinician Investigator Trainee Association of Canada (CITAC) has a significant interest in ensuring the training of clinician instigators, at both the undergraduate and residency levels, remains adept at meeting the challenges of today's health care system. In the August issue of Clinical Investigative Medicine, Appleton et al. published the first document reporting on the data collected by CITAC on the basic demographics of Canadian CI trainees [3]. The authors captured census data from each CI training program. This collaborative and national effort is a first and crucial step in understanding the strengths and potential pitfalls of Canadian CI training programs. The data presented will be used as a reference resource to improve training programs and facilitate future research.
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 | 0.031 | 0.055 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.021 | 0.010 |
| Scholarly communication | 0.015 | 0.011 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.006 | 0.014 |
| Insufficient payload (model declined to judge) | 0.022 | 0.005 |
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".