The Canadian Urban Environmental Health Research Consortium (CANUE): a national data linkage initiative
Bibliographic record
Abstract
IntroductionHealth and environmental exposure databases are generally siloed in different research institutions across Canada and integrating them for environmental health research is a considerable challenge. Facilitating the linkage of these databases is essential to provide new analytical opportunities and help create efficiencies for research on environmental determinants of health. Objectives and ApproachCANUE is a Canadian Institutes of Health Research-funded platform for supporting environmental health research. CANUE collates and generates standardized environmental data on air and noise pollution, land use, green/natural spaces, climate change/extreme weather, and socioeconomic conditions for every postal code in Canada and makes them freely available to researchers. Systems and procedures are being developed by CANUE to facilitate the sharing and integration of these extensive geospatial exposures with existing observational cohorts and administrative health databases across Canada. This linkage will enable investigators to test hypotheses on the interdependent associations of environmental features with health impacts or benefits. ResultsCANUE now hosts a dozen national exposure databases and related metadata files, and actively adds new regional and national datasets. Streamlined processes for data sharing have been developed to facilitate easy merging with health data. Substantial consultation has also taken place with a wide range of health data holders to establish appropriate processes for receiving and managing environmental data, with particular focus on addressing challenges presented by differing ethics, consent and confidentiality requirements. These processes help accelerate the research process by making analysis-ready data available to investigators, create opportunities to study how multiple environmental factors are linked to a wide range of health outcomes, and generally increase the use of health and population databases for environmental health research. Conclusion/ImplicationsThe CANUE collaborative model illustrates how the production of policy-relevant evidence can be advanced through better coordination among environmental health researchers and linkage with health databases. CANUE is improving the scientific potential and cost-effectiveness of research in environmental epidemiology through streamlining linkage and access to standardized exposure datasets.
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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 | 0.150 | 0.195 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.025 | 0.053 |
| Science and technology studies | 0.017 | 0.004 |
| Scholarly communication | 0.017 | 0.007 |
| Open science | 0.015 | 0.022 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.019 | 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".