Innovative strategic Canadian research training from TomorrOw's Research Cardiovascular Health Care Professionals (TORCH).
Bibliographic record
Abstract
Cardiovascular research training is experiential, and "skills" are traditionally acquired through a master-apprentice paradigm. The complexity of contemporary clinical research requires a new model for research training. Facilitated through a Strategic Training Program Initiative, the Canadian Institutes of Health Research (CIHR), with its partners the Alberta Heritage Foundation for Medical Research and the Heart and Stroke Foundation, supported the Universities of Alberta and Calgary to create a new and innovative training model. Tomorrow's Research Cardiovascular Health Professionals (TORCH) is an integrated 2-year program for health care professionals from diverse disciplines to be mentored toward careers as leaders in translational cardiovascular research, applying discovery to human health. This report describes the vision, mission, core values, objectives, design and curriculum of the program. Our vision is the development of a new generation of cardiovascular research clinician-scientists, with particular emphasis on thought, leadership and collaboration. The program incorporates 4 core values: innovation and discovery, a translational and transdisciplinary focus, an emphasis on collaboration and integration of research concepts, and the teaching of a core body of research knowledge coupled with real-world "survival" skills. The core curriculum, organized according to a cluster concept, traverses the 4 pillars of the CIHR. Through the medium of 1-hour weekly videoconferences, the curriculum cycles through case studies, seminars and a journal club in focused areas of cardiovascular research. Mentors in the TORCH program have diverse backgrounds that epitomize the transdisciplinary translational aspects of the program and are chosen for their proven record of research accomplishment and prior history of successful mentoring. The program has recruited 19 trainees from a broad cross-section of disciplines, integrating 2 University of Alberta campuses. The preliminary experience has been both favourable and gratifying.
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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.030 | 0.004 |
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".