Modal equity of accessibility to healthcare in Recife, Brazil
Bibliographic record
Abstract
In the context of increasing urbanization and income inequality, transport professionals in the Global South need to be prepared to effectively plan for the needs of various groups within the population, particularly for those regarding health and well-being. Accessibility is widely used as a performance measure for land use and transport systems; it measures people’s ease of reaching desired destinations and incorporates mode, time, and/or cost constraints. Considerable differences exist in the level of accessibility experienced by different mode users in reaching healthcare facilities, which calls for additional equity considerations given the prevailing socio-demographic characteristics of the users of various modes and the importance of healthcare facilities as a destination. In this study, we explore the distribution of accessibility to healthcare facilities by public transport and by car in Recife, Brazil, through an equity assessment to identify areas with low accessibility using these modes at different times of day. In general, the higher accessibility of public transport as well as greater modal equity was observed in central regions of Recife, whereas the periphery, where many low-income census tracts can be found, experiences significant inequity when it comes to access by both modes to healthcare facilities. This analysis allowed us to classify locations to access impoverished, access absolutely impoverished, and access impoverished by public transport areas, which can be targeted with appropriate land use and public transport policy interventions. This paper can be of value to professionals and researchers working toward equitable land use and transport systems in the Global South.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".