Transdisciplinary Understanding and Training on Research-Primary Health Care (TUTOR-PHC): Knowledge Mobilization Symposium
Bibliographic record
Abstract
The Transdisciplinary Understanding and Training on Research-Primary Heath Care (TUTOR-PHC) Knowledge Mobilization Symposium was held in London, Ontario on April 27 and 28th, 2023. This event brought together TUTOR-PHC alumni and current trainees, as well as patient-partners, mentors, and knowledge users from practice, policy-making, and research organizations, and funding agencies. The focus of the Symposium was on knowledge mobilization where TUTOR-PHC alumni and current trainees shared their research, and where all participants actively created an enhanced network of individuals committed to PHC research. The Symposium provided a venue where research results were translated into action by the co-creation of knowledge dissemination products via researchers working with knowledge users. This Symposium marked the twentieth year of TUTOR-PHC, which is a one-year, pan-Canadian interdisciplinary research capacity building program.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".