Perception and Interaction of Urban Medical Space from the Perspective of Technical Geography: A Case Study of Guangzhou, China
Bibliographic record
Abstract
Through a questionnaire survey and semistructured interviews, this paper adopts a structural equation model to analyze the impact of official hospital WeChat accounts on urban residents’ spatial perceptions and interactions with technology in Guangzhou, China. We find that official hospital WeChat accounts have a significant remodeling effect on urban residents’ spatial behaviors and perceptions, mainly affecting the improvement in medical efficiency and the expansion of medical scope and convenience. Such accounts also reduce the psychological distance between urban residents and hospitals. Furthermore, online medical service platforms restructure urban residents’ physical spatial movement, which is characterized by spatiotemporal compression and technical interaction. Spatiotemporal compression is manifested in the reduction in invalid time for residents in urban medical spaces and the expansion of the scope of medical treatment. Technical interaction is reflected in the mutual consultation and promotion between urban residents and technology. This paper aims to improve our understanding of the influence of technology on urban medical space and residents, and provides a reference for the optimization of urban space governance.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".