Environmental Health Tracking in Ontario, Canada: Progress toward Evidence-Based Environmental Health Policies
Bibliographic record
Abstract
The utility of environmental health tracking (EHT) in developing effective environmental health programs and policies has been demonstrated by the US CDC. In Ontario, progress towards a provincial EHT program began in 2011 with two proof of concept pilot projects. These interactive mapping applications, (1) contaminants in small drinking water systems and (2) urban noise, helped to build the capacity and expertise necessary for EHT. Consultations and needs assessments began in 2013. In a survey of 102 public health practitioners from across the province; 70% reported a lack of adequate data to examine environmental health topics in their area, 78% felt that establishing EHT in Ontario would help improve public health and 63% identified barriers to implementing EHT. Priority data gaps included: outdoor air (41%), built environment (29%), extreme weather (29%), contaminated sites (25%), radon (24%) and drinking water (22%). Reported barriers to EHT included: long-term funding, local level skill and expertise, data standardization and comparability, timeliness of data, IT infrastructure and relevance for rural areas. A workshop was held in early 2014 to further develop Ontario EHT. Workshop attendees included public health practitioners, data stewards of priority EHT data, academics, experts from outside Ontario, and local, provincial and federal levels of government. Facilitated discussions about the content, design and use of EHT highlighted numerous priority areas and next steps for this work. The system architecture for an Ontario EHT is currently being developed. Preliminary feedback on this multi-tier architecture has highlighted the importance of supporting tools and documents for EHT. Specifically, the need for education and communication materials, messaging systems that facilitate discussion and collaboration between local levels of public health, and adaptability of outputs for users with varying skill and expertise in environmental health.
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 | 0.021 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.014 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".