Bibliographic record
Abstract
BACKGROUND: Arctic residents can be exposed to a wide range of contaminants through consumption of traditional (country) foods (i.e. food from wild animals and plants that are hunted, caught or collected locally in the Arctic). Yet these foods provide excellent nutrition, promote social cohesion, meet some spiritual needs for connectedness to the land and water, reinforce cultural ties, are economically important and promote overall good health for many. The risk and benefit balance associated with the consumption of traditional Arctic foods is complicated to communicate and has been referred to as the "Arctic Dilemma". This article gives an update on health risk communication in the Arctic region. It briefly summarizes some research on risk communication methodologies as well as approaches to an evaluation of the outcomes of risk communication initiatives. It provides information on specific initiatives in several Arctic countries, and particularly those that were directed at Indigenous populations. This article also summarizes some international versus local risk communication activities and the complexity of developing and delivering messages designed for different audiences. Finally, the potential application of social media for risk communication and a summary of "best practices" based on published literature and a survey of Inuit in a few Arctic countries are described. CONCLUSION: Several of the risk communication initiatives portrayed in this article indicate that there is only limited awareness of the outcome of risk communication messages. In some cases, risk communication efforts appear to have been successful, at least when effectiveness is measured in an indirect way, for example, by lower contaminant levels. However, due to missing effectiveness evaluation studies, uncertainty remains as to whether a specific risk communication method was successful and could be clearly linked to behavioural changes that resulted in decreased contaminant exposure.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".