A Health Issue in Urban Politics After the Pandemic COVID-19 Outbreak
Bibliographic record
Abstract
Abstract Background The aim of this paper is to identify how the literature analyses (identifies, evaluates, forecasts, etc.) the relationship between health issues and urban policy in relation to pandemic COVID-19. Four main levels are identified in these cases: (1) direct demands for change in health care, (2) social issues, (3) spatial organization and (4) redefining the tasks of public authority in the face of the challenges identified. Methods The basic working method used in the study assumed a critical analysis of the literature on the subject. The time scope of the search covered articles from January 2020 to the end of August 2021 (thus covering the period of 3 pandemic waves). Combinations of keywords in the titles were used to search for articles. Results This process of article qualification ultimately yielded 240 articles for analysis. The health perspective pointed out the need to develop a balance between health care and economic costs and the need to coordinate different specialists/spheres of action. Conclusions Such a balance also requires social and spatial analyses, illustrating the diversity of the social situation in individual cities (and more broadly in urbanized areas, including sometimes vast suburbs) and its connection (both in terms of causes and effects) with the intensity of pandemics and other health threats.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".