Challenges and Opportunities of Integrating Human Health into the Environmental Assessment Process: The Canadian Experience Contextualised to International Efforts
Bibliographic record
Abstract
A scoping review of the literature was conducted to identify the most pressing issues pertaining to the application of Health Impact Assessment (HIA) and the integration of health concerns into the Environmental Assessment (EA) process in Canada and internationally. The issues identified include the need for government intervention, gaps in methodology and tools, limitations of capacity and expertise, poor intersectoral, disciplinary and public collaboration/participation, challenges of data quantification and analytic complexity, and the need for process efficiency. The issues presented were also contextualised to the status quo practice of EA in Canada and the Canadian Environmental Assessment Act (CEAA 2012). Recommendations were proposed as a starting point for improved integration. First, a commitment by the actors involved to the protection of human health — aligned with the core mandate of the CEAA. Second, the achievement of intersectoral, disciplinary and public collaboration, led by government, ideally the health sector. The case is made for a new era of Canadian leadership and innovation at the interface of health and EA.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".