PD50 Helping Innovators Navigate Policy And Regulatory Processes
Bibliographic record
Abstract
Introduction: As part of a national aging and technology network, AGE-WELL, one work group aims to promote the understanding of potential policy and regulatory hurdles among innovators and decision-makers. This study describes the development of resources to help innovators to navigate policy and regulatory systems to have their technologies adopted. Methods: A policy primer was created that highlights considerations for innovators during the innovation process (from development to implementation). The content of the policy primer was developed by identifying resources through the Canadian Agency for Drugs and Technology in Health Grey Matters search tool, and in consultation with legal and regulatory consultants. By surveying AGE-WELL technology-developing projects (n=15) we characterized the technologies being developed within the network. Survey questions included: intended end-user/purchaser of the technology and past/anticipated facilitators/barriers in their innovation process. The policy primer and survey data were combined to create tailored innovation maps with considerations for each technology being developed within the network. These materials were used to develop a beta website where users can receive information relevant to various innovation stages, as it pertains to their technology. We gained feedback about our materials via surveys and interviews with AGE-WELL technology developers. Results: The tailored innovation maps and website were seen as helpful resources for understanding policy and regulatory processes required for technology adoption. Technology developers expressed interest in gaining further access to these resources. Innovators desired additional resources about demonstrating value and measuring technology effectiveness. Conclusions: Resources were developed to help guide technology innovators through policy and regulatory processes; preliminary feedback suggests these were valued by innovators. Next steps include refining the website and releasing these resources to innovators beyond the AGE-WELL Network.
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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 | 0.011 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.059 | 0.016 |
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".