Healthy cities: a visual conceptual framework for moving health knowledge into urban planning practice
Bibliographic record
Abstract
Despite the increasing recognition of health as a fundamental dimension in urban design and planning, it remains insufficiently incorporated into urban planning practice and policymaking. This study reexamines the ‘Knowledge Translation’ (KT) process through a literature review (N = 53) to address the gap between the public health and urban planning arenas. By analyzing the key KT components – knowledge, guidance, and implementation – we identified additional factors influencing the process and highlighted gaps and opportunities for improvement. Building on these insights, we developed a visual conceptual framework that synthesizes existing knowledge and addresses critical gaps to support urban practitioners and policymakers in creating ‘healthy cities’. The framework conceptualizes KT as a dynamic, iterative process guided by three key drivers: (i) continuous interaction among knowledge, guidance and implementation, all tailored to local contexts and shaped by decision-making processes; (ii) interdisciplinary and cross-sector collaboration, including active engagement with local communities to create a shared vision of a healthy city; and (iii) a ‘control center’, that integrates these components, facilitates training, and ensures ongoing evaluation and calibration.
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.
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 | 0.020 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.008 |
| Science and technology studies | 0.005 | 0.023 |
| Scholarly communication | 0.016 | 0.014 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.003 | 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".