Awareness and perceptions of contaminants in the Dehcho and Sahtú Regions of the Northwest Territories
Bibliographic record
Abstract
The development and dissemination of health messaging is a critical component of reducing health disparities. Participants (n = 87) from a human biomonitoring study in six Dene communities responded to a survey about health communication regarding contaminants. The survey included questions on awareness of health messages and risk perceptions related to country foods and contaminants. The vast majority of participants reported eating country foods (99%) and heard that country foods had beneficial nutrients (90%). Seventy per cent of respondents had heard or seen messages about fish with high levels of mercury, and 60% had concerns about the safety or quality of country foods they consumed. Respondents who reported decreasing the number of fish they ate since hearing the messages about fish and mercury had lower (p = 0.04) mercury concentration in hair, compared to those who had not heard the messages. However, no differences in hair mercury were observed for respondents who reported to have changed their fishing location, chosen smaller fish or eaten less predatory fish since hearing the messages. Results indicate the need to examine reasons for self-reported behaviour changes, in addition to awareness. The conclusions of this study can inform the development of messaging and risk management decisions about contaminants within Indigenous populations.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".