Translating evidence into practice with the National Advisory Committee on Sexually Transmitted and Blood-Borne Infections
Bibliographic record
Abstract
For over 30 years, the Government of Canada has developed guidelines on sexually transmitted and blood-borne infections (STBBI) with a group of subject matter experts. This expert group provided advice to the Public Health Agency of Canada (PHAC) from 2004 to 2019; transitioning to the National Advisory Committee on STBBI (NAC-STBBI) in 2019. NAC-STBBI supports PHAC's mandate to prevent and control infectious diseases by providing advice for the development of STBBI guidelines. The methodology for developing the NAC-STBBI recommendations is evolving to a more rigorous, systematic and transparent process that is consistent with current standards in guideline development. It is also informed by-and aligned with-the methods of several other major guideline developers. The methodology incorporates the Grading of Recommendations, Assessment, Development and Evaluation (GRADE) approach, as appropriate, when conducting evidence reviews and developing recommendations. Recommendations will be published on the canada.ca website with the supporting NAC-STBBI Statement detailing the methodology and evidence used to develop them. This process will ensure that PHAC provides trustworthy evidence-based STBBI recommendations to primary care providers and public health professionals.
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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 | 0.305 | 0.602 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.017 | 0.015 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.021 | 0.008 |
| Open science | 0.010 | 0.012 |
| Research integrity | 0.018 | 0.027 |
| Insufficient payload (model declined to judge) | 0.012 | 0.009 |
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".