Choosing tools for building healthy spaces: an overview of guidance toolkits available from North America and Australia
Bibliographic record
Abstract
In response to the growing evidence supporting healthy built environments as a means of promoting health, several checklist-based knowledge translation tools have been designed for public health practitioners, urban planners and policy-makers. These tools have increased, but to date no comparison has been done of the quality or value of these tools to users for evaluating city plans for healthy built environment features. A search of both peer-reviewed literature and grey literature yielded 10 checklist-based tools from North America and Australia that were evidence-informed. The checklist criteria in each tool were evaluated using a thematic analysis. Themes were further classified into six settings. Tools were compared based on the breadth of settings assessed and usability for evaluating city plans. Our analysis identified that tools varied widely in the breadth of content with several tools addressing less than half the settings. Overall more recently published tools scored as more useful and in-depth. Transportation, urban design and open spaces were most addressed both within tools and across all tools. There is significant variation in quality and concepts covered across tools and no single tool is best used across all settings.
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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 | 0.045 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".