Applying precaution to environmental health issues at the local level: A proposed guide based on the research and experiences of Toronto Public Health
Bibliographic record
Abstract
While the Precautionary Principle (PP) is an important policy innovation relevant to public health, practitioners do not agree on how or when it should be applied. Action on environmental health issues at Toronto Public Health (TPH) has clearly been informed by the PP. We have recently developed a guide to applying precaution that can be used to assist local public health practitioners in decision making to address environmental health hazards in the community. We applied the Guide retrospectively to TPH case examples involving education, program, policy, legislative, and advocacy interventions to manage exposures to environmental hazards. This exercise served to refine the Guide and increase our understanding of how and when TPH has applied precaution in the past. Our Guide promises to be a useful decision making support tool that will help users (1) assess what degree of precaution is appropriate for a given context; (2) systematically document evidence about harms and exposures (including uncertainties) while making the assumptions about evidence more explicit and transparent; (3) highlight potential trade-offs (including consideration of both risks and benefits), explore alternatives, and assess feasibility of interventions; (4) plan adequate communication and stakeholder engagement; and (5) institute monitoring and evaluation so as to ensure interventions still meet users’ needs. We see the Guide as a tool that deepens the process of learning and enquiry on issue management in environmental health practice. We urge others to share their applications of the PP using our Guide to promote mutual learning.
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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 | 0.038 | 0.033 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.007 | 0.014 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.007 | 0.010 |
| Research integrity | 0.011 | 0.009 |
| Insufficient payload (model declined to judge) | 0.008 | 0.006 |
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".