Riding the Knowledge Translation Roundabout: Lessons Learned from the Canadian Institutes of Health Research Summer Institute in Knowledge Translation
Bibliographic record
Abstract
Background: Funding the education and training of the next generation of health researchers is a key mandate of the Canadian Institutes of Health Research (CIHR) knowledge translation (KT) portfolio. The field of KT is growing daily; thus, the training and development of a new generation of KT researchers is essential.\nMethods: Using curriculum documents, participant evaluations, and self-reflection, this paper describes a unique Summer Institute hosted by the CIHR in Cornwall, Ontario, Canada. We outline the key aspects of a successful training initiative that could inform organizations and agencies worldwide with an interest in or who have a mandate for KT.\nResults: This work provides potential funders, faculty, and students with an inside look into the purpose, process, and outcomes of such training initiatives.\nConclusions: National and international KT organizations, research institutions, and funding agencies are encouraged to consider replicating the training model employed here, as investment into KT personnel will foster the advancement of the field within and beyond local borders.'To the individual who devotes his/her life to science, nothing can give more happiness than when the results immediately find practical application. There are not two sciences. There is science and the application of science, and these two are linked as the fruit is to the tree.' - Louis Pasteur, 1871 (from presentation by Ian Graham, 2008 CIHR Knowledge Translation Summer Institute).
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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 | 0.151 | 0.095 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.031 | 0.035 |
| Scholarly communication | 0.025 | 0.019 |
| Open science | 0.008 | 0.019 |
| Research integrity | 0.009 | 0.021 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".