Strengthening the regulatory system through the implementation and use of a quality management system.
Bibliographic record
Abstract
Quality management systems (QMS), based on ISO 9001 requirements, are applicable to government service organizations such as Health Canada's Biologics and Genetic Therapies Directorate (BGTD). This communication presents the process that the BGTD followed since the early 2000s to implement a quality management system and describes how the regulatory system was improved as a result of this project. BGTD undertook the implementation of a quality management system based on ISO 9001 and containing aspects of ISO 17025 with the goal of strengthening the regulatory system through improvements in the people, processes, and services of the organization. We discuss the strategy used by BGTD to implement the QMS and the benefits that were realized from the various stages of implementation. The eight quality principals upon which the QMS standards of the ISO 9000 series are based were used by senior management as a framework to guide QMS implementation.
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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 | 0.167 | 0.143 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.014 | 0.007 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".