Using maps to communicate environmental exposures and health risks: Review and best-practice recommendations
Bibliographic record
Abstract
BACKGROUND: Graphical materials can be effective communication tools, and maps in particular are a potentially powerful means of conveying spatial information. Previous reviews have provided insights on the application of cartographic best practices, pitfalls to avoid, and considerations related to risk perception and communication, but none has reviewed primary studies of the effectiveness or utility of maps to users, nor have they addressed the issue from the perspective of health literacy, environmental health literacy, or public health ethics. OBJECTIVES: To systematically identify and review the literature pertaining to evaluation of maps in general, or specific map features, as environmental exposure and health risk communication tools; to formulate best-practice recommendations; and to identify future research priorities. METHODS: A health science librarian searched the literature for commentaries, reviews, and primary studies. Titles, abstracts, and full-text papers were screened for inclusion, and details of methods and results were extracted from 4 reviews and commentaries and 18 primary studies. This was supplemented by one additional review and 13 additional primary studies pertaining to use of maps for communication about wildfires and floods. One additional paper was identified by reviewing reference lists of all relevant papers. RESULTS: and Discussion: While there are significant gaps in the evidence, we formulated best practice recommendations highlighting the perspectives of health literacy and environmental health literacy. Key recommendations include: understanding the map developer's societal role and mental model underlying map design; defining, understanding and iteratively engaging with map users; informing map design using key theoretical constructs; accounting for factors affecting risk perception; adhering to risk communication principles and cartographic best practices; and considering environmental justice and public health ethics implications. Recommendations for future research are also provided.
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 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 | 0.071 | 0.193 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.008 | 0.011 |
| Bibliometrics | 0.023 | 0.019 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.010 | 0.017 |
| Open science | 0.011 | 0.007 |
| Research integrity | 0.011 | 0.009 |
| Insufficient payload (model declined to judge) | 0.015 | 0.005 |
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".