Exploring the role of trust in health risk communication in Nunavik, Canada
Bibliographic record
Abstract
Abstract Communicating about health risks in the Arctic can be challenging. Numerous factors can hinder or promote effective communication. One of the most important components in effective communication is trust in an information source. This is particularly true when a risk is unfamiliar or complex because the public must rely on expert assessment rather than personal evaluation of the risk. A total of 112 Inuit residents from Nunavik, Canada, were interviewed to better understand the factors that influence trust in individuals or organisations. Results indicate that there are six primary factors that influence trust in an information source. These factors include: (1) whether the information source is a friend or family member; (2) past performance of the individual or organisation; (3) the general disposition of the audience member (that is, he or she believes that most people are trustworthy); (4) the openness or candidness of the source; (5) value similarity (referring to the perceived correspondence in values between the audience member and communicator); and (6) the credibility of the source. The results of this study can help determine who or what agencies should provide messages about health risks in the Arctic. It also provides insight about effective strategies for engendering trust among Arctic residents.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.014 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".