Expert and non-expert perceptions of risk: Improving the risk communication of cancer
Bibliographic record
Abstract
Cancer clusters constitute geographical areas where the frequency of cancer diagnoses during a given period of time occur more frequently than expected by chance. Cancer clusters can impact perceptions of risk and generate significant anxiety in communities. Unfortunately, cluster investigations rarely yield the answers citizens seek around a definitive cause of cancer due to the long latency of cancer and other factors. As a result, health officials may appear to be withholding information and not doing enough to address public concerns. Effective cancer risk communication may also be hindered by other stakeholders such as the media, who sometimes sensationalize risks from environmental hazards, which can distort the public’s perceptions of risks. The result may be a community dissatisfied with a cluster investigation’s results, or worse, a community that distrusts local leaders and doesn’t understand the information reported by expert officials. The four studies comprising this dissertation aimed to summarize key issues with the communication of and investigation of cancer clusters in Canada; test the impact of different types of cancer information on risk perceptions; and explore whether individual characteristics and skills were linked to positive attitudes about coping with cancer risks. An analysis of cancer news coverage and interviews with Canadian public health officials revealed that communities may be receiving inadequate and inconsistent information about cancer risks during cluster investigations. In addition, an experiment and survey revealed information trustworthiness and individuals’ numeracy and health literacy to be important factors shaping cancer risk perceptions and attitudes. This work has significant implications for risk communicators and educators seeking improved methodologies of cancer risk communication and risk education to (1) manage differences in cancer risk perceptions between experts and non-experts (2) enhance public trust in institutions and perceptions of expert competence and (3) inform future educational interventions that promote cancer coping beliefs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.080 | 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 teacher head, 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".