Occupations at risk of contracting zoonoses of public health significance in Québec
Bibliographic record
Abstract
INTRODUCTION: Climate change plays an important role in the geographic spread of zoonotic diseases. Knowing which populations are at risk of contracting these diseases is critical to informing public health policies and practices. In Québec, 14 zoonoses have been identified as important for public health to guide the climate change adaptation efforts of decision-makers and researchers. A great deal has been learned about these diseases in recent years, but information on at-risk workplaces remains incomplete. The objective of this study is to paint a portrait of the occupations and sectors of economic activity at risk for the acquisition of these zoonoses. METHODS: , giardiasis, listeriosis, salmonellosis, Eastern equine encephalitis, Lyme disease, West Nile virus, food botulism, Q fever, avian and swine influenza, rabies, hantavirus pulmonary syndrome) and occupational health. The literature search retrieved 12,558 articles and, after elimination of duplicates, 6,838 articles were evaluated based on the title and the abstract. Eligible articles had to address both concepts of the research issue (prioritized zoonoses and worker health). Of the 621 articles deemed eligible, 110 were selected following their full reading. RESULTS: Of the diseases under study, enteric zoonoses were the most frequently reported. Agriculture, including veterinary services, public administration services and medical and social services were the sectors most frequently identified in the literature. CONCLUSION: The results of our study will support public health authorities and decision-makers in targeting those sectors and occupations that are particularly at risk for the acquisition of zoonoses. Doing so will ultimately optimize the public health practices of those responsible for the health of workers.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.000 |
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".