Communication about environmental health risks: A systematic review
Bibliographic record
Abstract
BACKGROUND: Using the most effective methods and techniques for communicating risk to the public is critical. Understanding the impact that different types of risk communication have played in real and perceived public health risks can provide information about how messages, policies and programs can and should be communicated in order to be most effective. The purpose of this systematic review is to identify the effectiveness of communication strategies and factors that impact communication uptake related to environmental health risks. METHODS: A systematic review of English articles using multiple databases with appropriate search terms. Data sources also included grey literature. Key organization websites and key journals were hand searched for relevant articles. Consultation with experts took place to locate any additional references.Articles had to meet relevance criteria for study design [randomized controlled trials, clinical controlled trials, cohort analytic, cohort, any pre-post, interrupted time series, mixed methods or any qualitative studies), participants (those in community-living, non-clinical populations), interventions (including, but not limited to, any community-based methods or tools such as Internet, telephone, media-based interventions or any combination thereof), and outcomes (reported measurable outcomes such as awareness, knowledge or attitudinal or behavioural change). Articles were assessed for quality and data was extracted using standardized tools by two independent reviewers. Articles were given an overall assessment of strong, moderate or weak quality. RESULTS: There were no strong or moderate studies. Meta-analysis was not appropriate to the data. Data for 24 articles were analyzed and reported in a narrative format. The findings suggest that a multi-media approach is more effective than any single media approach. Similarly, printed material that offers a combination of information types (i.e., text and diagrams) is a more effective than just a single type, such as all text. Findings also suggest that factors influencing response to risk communications are impacted by personal risk perception, previous personal experience with risk, sources of information and trust in those sources. CONCLUSIONS: No single method of message delivery is best. Risk communication strategies that incorporate the needs of the target audience(s) with a multi-faceted delivery method are most effective at reaching the audience.
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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 | 0.022 | 0.080 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.010 |
| Bibliometrics | 0.015 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".