Smart City and High-Tech Urban Interventions Targeting Human Health: An Equity-Focused Systematic Review
Bibliographic record
Abstract
Urban infrastructure systems responsible for the provision of energy, transportation, shelter, and communication to populations are important determinants of health and health equity. The term "smart city" has been used synonymously with other terms, such as "digital city", "sustainable city", and "information city", even though definitional distinctions exist between terms. In this review, we use "smart cities" as a catch-all term to refer to an emerging concept in urban governance practice and scholarship that has been increasingly applied to achieve public health aims. The objective of this systematic review was to document and analyze the inclusion of equity considerations and dimensions (i.e., a measurement, analytical, or dialectical focus on systematic disparities in health between groups) in smart city interventions aimed to improve human health and well-being. Systematic searches were carried out in the Cumulative Index to Nursing and Allied Health Literature (CINAHL), Psychological Information Database (PsycINFO), the PubMed database from the National Center for Biotechnology Information, Elsevier's database Scopus, and Web of Science, returning 3219 titles. Ultimately, 28 articles were retained, assessed, and coded for their inclusion of equity characteristics using the Cochrane PROGRESS-Plus tool (referring to (P) place of residence, (R) race, (O) occupation, (G) gender, (R) religion, (E) education, (S) socio-economic status (SES), and (S) social capital). The most frequently included equity considerations in smart city health interventions were place of residence, SES, social capital, and personal characteristics; conversely, occupation, gender or sex, religion, race, ethnicity, culture, language, and education characteristics were comparatively less featured in such interventions. Overall, it appears that most of intervention evaluations assessed in this review are still in the early testing phases, and thus did not include or feature robust evaluative designs or commercially available technologies.
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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 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".