Implications of antibiotic use in salmon aquaculture for the health of workers and their communities in Canada: a multi-method thesis
Bibliographic record
Abstract
Antimicrobial resistance (AMR) is a global health crisis. Research and AMR surveillance gaps in food animal production, a major driver, can lead to inequitable exposure risks encountered by workers and communities. This thesis includes a literature review summarizing occurrences of antibiotics and AMR in salmon aquaculture, a thematic analysis of interviews with Canadian salmon aquaculture stakeholders identifying key socio-ecological interactions influencing risk mitigation behaviours, and a retrospective cross-sectional analysis of secondary health data providing AMR rates in Newfoundland and Labrador (NL) – a salmon producing Canadian province. AMR occurs across all areas of salmon production. Important factors influencing reporting gaps and attitudes towards antibiotics and AMR as occupational hazards in salmon aquaculture include non-binding policy targeting reductions in antibiotic use and a values-based rivalry between the federal and provincial governments and the industry. Top-down priority setting for AMR risk mitigation strategies requires the coordinated involvement of public, occupational health, and food-animal production stakeholders. Harmonized AMR surveillance programs will help identify occupational and population cohorts at greater exposure risk, exposure pathways, and enhance food-production and distribution hygiene. AMR infections among NL residents most frequently involves enteric bacterial infections with highest impacts on rural residents, identifying important targets of risk mitigation programs.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".