Research and Monitoring Efforts on First Nations Environmental Health Issues
Bibliographic record
Abstract
The First Nations and Inuit Health Branch, Health Canada/Indigenous Services Canada has been working on several large programs to assist First Nations in understanding and reducing the impact of exposure to chemical hazards in their environment: the First Nations Environmental Contaminants Program (FNECP); the First Nations Food, Nutrition and Environment Study (FNFNES) and the First Nations Biomonitoring Initiative.Initially, the Mercury Biomonitoring Program, which ran from 1970 to 2000, monitored exposure to mercury by collecting over 70,000 blood and hair samples in over 500 First Nation.The FNECP, created in 1999, supports community-based monitoring, research and risk assessment. Since 2000, the national FNECP has funded 103 national projects. Through dietary surveys and chemical exposure assessments and/or human biomonitoring, First Nations collaborated with researchers to gain important information on the chemical safety of their traditional diet. As appropriate, recommendations were made with respect to traditional food consumption.The FNFNES was created in 2008 to fill knowledge gaps on the diet and safety of traditional foods for First Nations living on-reserve south of the 60th parallel. This study was implemented region by region from 2008 to 2018. The FNFNES included five components: household interviews; drinking water sampling for trace metals; hair sampling for mercury; surface water sampling for pharmaceuticals and traditional food sampling for chemical contaminant levels.Results from the FNFNES mercury in hair sampling program will be compared to the findings of the earlier methylmercury biomonitoring program.Results from the FNFNES pharmaceutical sampling results will be explained in relation to how mixtures of pharmaceuticals can be characterized with respect to both their ecological and human health risks.
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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 | 0.025 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 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".