Summary of the Federal Action Plan on Antimicrobial Resistance and Use in Canada
Bibliographic record
Abstract
In October 2014, the Government of Canada released Antimicrobial Resistance and Use in Canada: A Federal Framework for Action and has recently followed up with its Federal Action Plan on Antimicrobial Resistance and Use in Canada.The Federal Action Plan outlines concrete deliverables in support of the three areas of focus identified in the Federal Framework.Highlights of the work that will be undertaken by the Public Health Agency of Canada include: establishing the Canadian Antimicrobial Resistance Surveillance System to strengthen coordination and integration of antimicrobial resistance (AMR) and antimicrobial use (AMU) activities and information; undertaking a scan to identify potential gaps in infection prevention and control practices; and building on lessons learned from the November 2014 AMR awareness campaign to inform future public awareness and education activities.The Government of Canada remains committed to taking action on AMR and AMU and will continue to identify new activities to help combat the spread of AMR.The Federal Action Plan is an evergreen document that will be updated regularly to keep Canadians informed of activities and ongoing progress in implementing the Federal Framework.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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".