Creating a knowledge translation trainee collaborative: from conceptualization to lessons learned in the first year
Bibliographic record
Abstract
Trainees (e.g., graduate students, residents, fellows) are increasingly identifying knowledge translation as their research discipline. In Canada, a group of trainees have created a trainee-initiated and trainee-led national collaborative to provide a vehicle for trainees to examine the diversity of knowledge translation research and practice, and to link trainees from diverse geographical areas and disciplines. The aim of this paper is to describe our experience and lessons learned in creating the Knowledge Translation Trainee Collaborative. In this meeting report, we outline the process, challenges, and opportunities in planning and experiencing the collaborative's inaugural meeting as participant organizers, and present outcomes and learnings to date.
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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 | 0.121 | 0.085 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.020 | 0.015 |
| Scholarly communication | 0.022 | 0.021 |
| Open science | 0.006 | 0.027 |
| Research integrity | 0.008 | 0.014 |
| Insufficient payload (model declined to judge) | 0.004 | 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".