349 Translation of novel multidisciplinary health technologies in the Ontario healthcare system: A case study of pharmacogenomic testing
Bibliographic record
Abstract
OBJECTIVES/GOALS: There is a need for high-quality and efficient translation of health technologies in the Ontario healthcare system. The goal of this project is to understand the decision-making processes of government expert groups developing recommendations for the system-level implementation of pharmacogenomic testing. METHODS/STUDY POPULATION: This prospective observational case study includes the Ontario Health Pharmacogenomics (PGx) Working Group focused on developing recommendations for a PGx testing implementation strategy in the province. Ontario Health is the government agency that oversees provincial healthcare planning and service delivery. Using qualitative ethnographic methods, we will observe and document the working group’s activities over a 10-month period. Data collection involves meeting recordings, correspondences, researcher field notes, decision-making processes, and group characteristics. Using descriptive statistics and inductive qualitative analyses, the data will be examined to build theory and frameworks for knowledge translation. RESULTS/ANTICIPATED RESULTS: The results will be presented through a case report, process maps, decision milestones, visualizations, and procedural recommendations for future expert groups. This study will contribute to the body of foundational knowledge about translational sciences and support the National Center for Advancing Translational Sciences’guiding principles. To enhance translational processes and train the future translational workforce, this research can be used for educational initiatives. In addition, the observed processes will inform a theory about how expert recommendations are developed in public healthcare systems. DISCUSSION/SIGNIFICANCE: This research addresses a current gap in understanding around translational processes, government decision-making, and the development of recommendations for the adoption, implementation, and dissemination of the novel health technologies transforming public healthcare in Canada.
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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 | 0.014 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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; a candidate call from one teacher head, 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".