Tool for assessing health and equity impacts of interventions modifying air quality in urban environments
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Urban outdoor air pollution (AP) is a major public health concern but the mechanisms by which interventions impact health and social inequities are rarely assessed. Health and equity impacts of policies and interventions are questioned, but managers and policy agents in various institutional contexts have very few practical tools to help them better orient interventions in sectors other than the health sector. Our objective was to create such a tool to facilitate the assessment of health impacts of urban outdoor AP interventions by non-public health experts. METHODS: An iterative process of reviewing the academic literature, brainstorming, and consultation with experts was used to identify the chain of effects of urban outdoor AP and the major modifying factors. To test its applicability, the tool was applied to two interventions, the London Low Emission Zone and the Montréal BIXI public bicycle-sharing program. RESULTS: We identify the chain of effects, six categories of modifying factors: those controlling the source of emissions, the quantity of emissions, concentrations of emitted pollutants, their spatial distribution, personal exposure, and individual vulnerability. Modifiable and non-modifiable factors are also identified. Results are presented in the text but also graphically, as we wanted it to be a practical tool, from pollution sources to emission, exposure, and finally, health effects. CONCLUSION: The tool represents a practical first step to assessing AP-related interventions for health and equity impacts. Understanding how different factors affect health and equity through air pollution can provide insight to city policymakers pursuing Health in All Policies.
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.
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 | 0.012 | 0.001 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 it