CHSRF Knowledge Transfer: Organizational Value in Enhancing Individual Research Use Capacity: A Joint Evaluation Project led by EXTRA and SEARCH Canada
Bibliographic record
Abstract
Evidence-informed decision-making supports high-quality, efficient healthcare. Programs such as SEARCH Classic (Swift Efficient Application of Research in Community Health) and EXTRA (Executive Training for Research Application) give health system decision-makers the skills and experience required to apply the best evidence to their work. But effectively leading change in how evidence comes to bear on the overall management and delivery of care requires strategies aimed at whole organizations and systems. The Canadian Health Services Research Foundation (CHSRF, EXTRA's managing organization) and SEARCH Canada (the SEARCH Classic program's managing organization) recently launched a jointly commissioned research study to assess organizational mechanisms and the impacts of these programs. Moving away from a focus on individual trainees and their immediate organizational connections, this evaluation builds on the evidence to date that leads to the hypothesis that a critical mass of highly educated, evidence-savvy decision-makers (senior executives in the case of EXTRA; middle- and front-line managers in the case of SEARCH Canada) enhance organizational capacity to use knowledge and ultimately lead to a more systematic use of evidence at the systems level (Champagne et al. 2008).
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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 | 0.264 | 0.209 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".