Investigating reports of cancer clusters in Canada: a qualitative study of public health communication practices and investigation procedures
Bibliographic record
Abstract
Introduction Public health officials provide an important public service responding to community concerns around cancer and often receive requests to investigate patterns of cancer incidence and communicate findings with citizens. In this study, we identified procedures Canadian public health officials followed when investigating reports of cancer clusters, and explored the challenges officials faced conducting risk communication with communities. Methods Thirteen interviews were administered by telephone with 15 officials across Canadian jurisdictions and analyzed using thematic analysis. A content analysis of procedural documents received from five provinces was also undertaken. Results A third of provinces/territories in this study did not use any consistent guidelines to investigate reports of cancer clusters, a third used their own guidelines and a third used guidelines from other countries. Each Canadian jurisdiction identified a different agency or individual responsible for investigating cluster inquiries. Officials in most interviews considered public education to be the primary objective of risk communication during an investigation. Officials in only 4 of 13 interviews cited an overall positive response from the public after investigating reports of a cancer cluster. Conclusion Differences in practices used to investigate suspected cancer clusters by public health officials were revealed in this work. Establishing pan-Canadian cancer cluster guidelines could improve procedural consistency across jurisdictions and offer enhanced opportunities to compare cluster responses for evaluation. A reporting system to track reported clusters may improve information sharing between federal, provincial/territorial and local investigators. During formal investigations, face-to-face participatory communication approaches should be explored to improve citizen engagement and manage community concerns.
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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 | 0.021 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.027 | 0.016 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".