An adaptable Health Impact Assessment (HIA) framework for assessing health within Environmental Assessment (EA): Canadian context, international application
Bibliographic record
Abstract
One of the most widely used approaches for assessing environmental effects of large-scale projects is Environmental Assessment (EA). Recently, there has been a focus on including broader health impacts as part of the EA process. One of the tools available to achieve this is Health Impact Assessment (HIA). In order to address the issue of developing a consistent and transparent method for HIA, an assessment framework was developed with the intention of: (1) ensuring that the framework can be used as a stand-alone process and when integrated with EA; (2) applying language to closely align with EA processes; and, (3) devising a system for evaluating overall impact when a multitude of determinants are considered. The Assessment Framework is presented along with a decision matrix to help to determine potential significance of health outcomes. It also provides a process for characterization of effects and identifies whether outcomes are significant. By using an HIA Framework, a well-known yet underutilized tool can effectively address health issues within the EA process, both in a Canadian context and internationally.
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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 | 0.022 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".