Building a framework for the evaluation of knowledge translation for the Canadian Network for Observational Drug Effect Studies
Bibliographic record
Abstract
PURPOSE: The Canadian Network for Observational Drug Effect Studies (CNODES), a network of pharmacoepidemiologists and other researchers from seven provincial sites, provides evidence on the benefits and risks of drugs used by Canadians. The Knowledge Translation Team, one of CNODES' four main teams, evaluates the impact of its efforts using an iterative and emergent approach. This article shares key lessons from early evaluation phases, including identifying stakeholders and their evaluation needs, choosing evaluation theories and approaches, and developing evaluation questions, designs, and methods appropriate for the CNODES context. METHODS: Stakeholder analysis was conducted using documentary analysis to determine key contextual factors and research evidence needs of decision maker partners and other stakeholders. Selected theories and frameworks from the evaluation and knowledge translation literature informed decisions about evaluation design and implementation. A developmental approach to evaluation was deemed appropriate due to the innovative, complex, and ever-changing context. RESULTS: A theory of change, logic model, and potential evaluation questions were developed, informed by the stakeholder analysis. Early indicators of program impact (citation metrics, alternative metrics) have been documented; efforts to collect data on additional indicators are ongoing. CONCLUSION: A flexible, iterative, and emergent evaluation approach allows the Knowledge Translation Team to apply lessons learned from completed projects to ongoing research projects, adapt its approaches based on stakeholder needs, document successes, and be accountable to funders/stakeholders. This evaluation approach may be useful for other international pharmacoepidemiology research networks planning and implementing evaluations of similarly complex, multistakeholder initiatives that are subject to constant change.
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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 | 0.064 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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; both teacher heads 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".