Integrating Health into Transportation Planning: A Tiered Framework for Local Governments
Bibliographic record
Abstract
Integrating health into transportation planning is a challenge for many local governments. One of the reasons is the lack of knowledge about and access to data collected in the health and transportation sectors that can be used to inform decision-making. Some local governments are quite advanced in their active transportation data collection and use whereas others are at the very early stages. This results in differences in next steps for integrating health considerations. Using the Greater Vancouver area as an example, the authors designed a framework that consists of three stages along a continuum of ‘readiness’ and tailored steps to integrate health into transportation planning. Each tier describes the extent to which data are currently collected at the local level, and is connected with the data needs, promising practices on how to obtain those data and tier-specific recommendations for next steps. For example, local governments at tier 1 typically collect limited or no data on (active) transportation, are interested in having better transportation data and would likely benefit from existing methodology to collect these data in a standardized format. Tier 2 local governments collect some active transportation data and use some health data sources available, such as traffic injury databases. Tier 3 municipalities collect large amounts of active transportation data and are interested in assessing the longer term health impacts of transportation decisions that go beyond injury prevention. This tiered framework is a practice-ready tool that can facilitate municipal and regional planners and engineers in moving forward with integrating health into transportation
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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 | 0.056 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.007 | 0.013 |
| Scholarly communication | 0.023 | 0.015 |
| Open science | 0.008 | 0.016 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".