The Burden of Treatment Among Delhi’s Homeless: An Analysis of Street Medicine Consultations
Bibliographic record
Abstract
Background: The health challenges faced by the homeless are widely unaccounted for in the global south. In India, the lack of a primary healthcare sector in urban areas has led informal healthcare providers, such as Street Medicine, to step in. Methods: By compiling data collected by the Centre for Equity Studies’ Street Medicine teams from June 2016 to October 2018 (n =16,635), this study provides the first empirical assessment of the homeless disease burden in a global south country, while limited to only the people experiencing homelessness that the teams treat, hence being a burden of treatment. Our analysis quantifies this burden among those who seek care from Street Medicine teams and identifies variations in this burden’s distribution across demography and time. Results: The majority (n = 13,557; 81.5%) of Street Medicine cases can be attributed to 19 diagnoses or symptoms, which are mostly therapeutically-simple conditions. The distribution of disease seems to be affected by different configurations of three characteristics: demographics (age and sex), urban geography (where homeless reside), and season. Conclusion: The Street Medicine teams must reflect on the balance they wish to achieve by addressing the relatively common and mild conditions documented in the dataset, and more severe and established diseases within homeless communities. Rapid diagnostic tests for resource-constrained settings could be integrated into Street Medicine practice in order to strengthen the data on which resource allocation decisions are made and improve assessments of homeless disease burden.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".