Federal public health strategies to minimize the importation of communicable diseases into Canada
Bibliographic record
Abstract
Background: The global spread of communicable diseases is a growing concern largely as a result of increased international travel.In Canada, although most public health management of communicable diseases occurs at the front line, the federal government also takes actions to prevent and mitigate their importation.Objective: To describe the role of the Public Health Agency of Canada (PHAC) in minimizing the importation of communicable diseases through preventive measures taken before travellers leave Canada and through early detection and prompt containment measures taken when travellers arrive in the country with a potential communicable disease.Interventions: PHAC works to minimize the importation of communicable diseases into Canada by developing evidence-based travel health advice and targeted outreach activities geared to the public and to health care professionals.On the basis of the Quarantine Act and the International Health Regulations (2005), PHAC also conducts inspections of conveyances such as aircraft and boats and works with partners to conduct border screening to assess ill travellers entering the country.Conclusion: PHAC plays an important role in preventing and minimizing the importation of communicable diseases into Canada in conjunction with clinicians, public health authorities at all levels of government and other federal government departments.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".