Using Health Impact Assessments to Assess Potential Health Impacts of Local Infrastructure Projects: A Case Study
Bibliographic record
Abstract
Rapid population growth in Ontario has resulted in a need for new infrastructure, including wastewater treatment plants (WWTPs). In support of a previously completed Environmental Assessment for a proposed WWTP, a Health Impact Assessment (HIA) is currently being conducted to assess the potential positive and negative health impacts due to the project to a First Nations Community (FNC). The HIA process is complementary to existing risk assessment and environmental assessment protocols where human health is viewed, not only from the perspective of environmental exposure to chemicals in the built and natural environments, but in terms of various health determinants such as physical health, mental well-being, and social, cultural and economic factors. The ongoing HIA, while providing a platform to enhance communication and address the concerns of the FNC, will recommend measures to enhance the potential positive impacts and mitigate any negative impacts due to the project. To determine the scope of the HIA, potential health issues of concern to the FNC due to the proposed WWTP, were identified and then reviewed through discussions with the FNC, public health and municipal officials, and other stakeholders. The engagement process identified that the main health determinants to be assessed in the HIA include: surface water quality with regards to pharmaceuticals and personal care products, food security, climate change, access to drinking water, impacts to cultural values and traditions, and the social and mental wellbeing of the FNC. Although this HIA is ongoing, initial results indicate that the HIA process will play an important role in establishing an unbiased means of communication between municipalities and FNCs. In general, HIAs are not intended to be advocacy tools; rather they are intended to provide further consideration of potential health outcomes of a given project or policy.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".