Identifying Priorities for Communicating a Large Body of Research for Impact
Bibliographic record
Abstract
Background CAREX (CARcinogen EXposure) Canada’s mandate is to communicate a body of academic research and expertise on Canadians’ exposures to carcinogens, to inform efforts to reduce exposures and ultimately reduce the risk of cancer. With 80 known and suspected carcinogens in its database and over 800 estimates of how and where Canadians are exposed, CAREX’s challenge has been to focus its efforts to achieve impact. Analysis A process model for identifying and prioritizing opportunities for knowledge translation was developed. From 2012-2017 that model was used to identify exposure priorities, select and engage knowledge users with readiness to collaborate, and explore opportunities to apply CAREX’s knowledge and expertise. Conclusion and implications A total of 54 impacts were tracked, including priority setting, cancer prevention research, implementation research, and policy and practice change.RésuméContexte CAREX Canada (CARcinogen EXposure) a pour mandat de communiquer la recherche et l’expertise académiques sur l’exposition des Canadiens aux carcinogènes, de soutenir les efforts pour réduire cette exposition, et en fin de compte de réduire les incidences du cancer. Dans sa base de données, CAREX recense quatre-vingts cancérogènes connus et soupçonnés et plus de huit cents estimations sur comment et où les Canadiens y sont exposés. Son défi principal a été de focaliser ses efforts afin d’avoir un meilleur impact. Analyse Un modèle de processus a été développé pour identifier et prioriser les occasions d’effectuer une application des connaissances. Entre 2012 et 2017, ce modèle a servi à identifier les priorités pour l’exposition aux cancérigènes, à sélectionner et intéresser des utilisateurs des connaissances prêts à collaborer, et à explorer les occasions pour appliquer le savoir et l’expertise de CAREX. Conclusion et implications On a relevé un total de 54 impacts, y compris l’établissement des priorités, la recherche sur la prévention du cancer, la recherche sur la mise en oeuvre, et la modification de politiques et de pratiques.
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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 | 0.221 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.013 | 0.012 |
| Science and technology studies | 0.011 | 0.007 |
| Scholarly communication | 0.026 | 0.020 |
| Open science | 0.004 | 0.017 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.013 | 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".