Mapping health in the Great Lakes areas of concern: a user-friendly tool for policy and decision makers.
Bibliographic record
Abstract
The role of the physical environment as a determinant of health is a major concern reported by the general public as well as by many policymakers. However, it remains one of the health determinants for which few available measures or indicators are readily available. This lack of data is compounded by the fact that evidence for direct cause-and-effect relationships in the literature is often equivocal, leading to feelings of uncertainty among the lay public and often leading to indecision among policymakers. In this article we examine one aspect of the physical environment--water pollution in the Great Lakes Areas of Concern (AOCs)--and its potential impacts on a wide range of (plausible) human health outcomes. Essentially, the International Joint Commission, the international agency that oversees Great Lakes water quality and related issues, worked with Health Canada to produce a report for each of the 17 AOCs on the Canadian side of the Great Lakes, outlining a long list of health outcomes and the potential relationships these might have with environmental exposures known or suspected to exist in the Great Lakes basin. These reports are based solely on secondary health data and a thorough review of the environmental epidemiologic literature. The use of these reports by local health policymakers as well as by public health officials in the AOCs was limited, however, by the presentation of vast amounts of data in a series of tables with various outcome measures. The reports were therefore not used widely by the audience for whom they were intended. In this paper we report the results of an undertaking designed to reduce the data and present them in a more policy-friendly manner, using a geographic information system. We do not attempt to answer directly questions related to cause and effect vis-à-vis the relationships between environment and health in the Great Lakes; rather, this work is a hypothesis-generating exercise that will help sharpen the focus of research into this increasingly important area of public health concern.
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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 | 0.011 | 0.050 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.119 | 0.042 |
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".