A two-stage concept mapping for emerging concepts: an analysis of the Smart Healthy City
Bibliographic record
Abstract
Rapid urbanization and technological advancement pose complex challenges to urban health governance, particularly amid demographic aging, environmental pressures, and widening health inequalities. While Smart Healthy Cities (SHCs) offer a promising paradigm to address these issues, current models lack a comprehensive, theoretically grounded framework for implementation. This study defines the SHC concept and examines its relevance for building inclusive, age-friendly urban environments. An innovative two-stage concept mapping methodology was employed, integrating qualitative insights from expert-focused group interviews with quantitative analysis using multidimensional scaling and hierarchical cluster analysis. A diverse panel of experts from public health, urban planning, digital innovation, and governance participated in the process. Four key dimensions of SHCs were identified: Healthy Environment Cities (emphasizing physical infrastructure), Smart Networking Cities (focusing on digital connectivity), Socially Sustainable Cities (advancing inclusive policies), and Health Empowering Cities (supporting individual capabilities and preventive health). These dimensions were found to contribute differentially to three core SHC objectives: health equity, smart connectivity, and system-level resilience. Priority concepts included improved healthcare access, intergenerational technology integration, and lifespan-oriented disease prevention. Pattern matching and go-zone analyses revealed a notable discrepancy: social sustainability, while conceptually important, was under-prioritized in implementation. The framework incorporates six theoretical perspectives—socio-ecological theory, smart city theory, health equity, systems thinking, the capabilities approach, and participatory urban planning—offering a multidimensional and systems-informed model. By conceptualizing cities as complex adaptive systems, this framework aligns digital innovation with equity and resilience goals. It provides urban planners and policymakers with a roadmap to develop inclusive, sustainable, and health-promoting cities. The study also contributes to Smart Healthy Age-Friendly Environment (SHAFE) discourse by expanding its application beyond aging populations to all urban residents.
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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 | 0.012 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".