ArticleInternational journal of environmental research and public health2026
Walkable Intelligent Parks: Can a Large Language Model Turn Urban Park Audit Findings into Actionable Recommendations?
Article in International journal of environmental research and public health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Park audits can inform neighbourhood-scale urban design strategies that support health. However, interpreting park audit data is time-consuming and requires specialised expertise. Recent advances in large language models suggest potential for assisting with structured data interpretation, but their application to park audits remains unexplored. This study examined whether a large language model (ChatGPT-5) can assist in interpreting park audit data from public parks in Dhaka City, Bangladesh. Using a modified park audit tool, 59 parks were audited, of which 26 met the predefined completeness threshold and were included in the analysis. ChatGPT-5 and human experts received identical park audit datasets and instructions for interpretation. Outputs were evaluated using content analysis and a pre-defined scoring framework to assess accuracy and comprehensiveness. Paired
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.