Applications of geospatial analyses in health research among homeless people: A systematic scoping review of available evidence
Bibliographic record
Abstract
Abstract Background The coronavirus pandemic visualized the inequality in the community living standards and how housing is a fundamental requirement to ensure a livable environment. However, even before the pandemic, unequal housing access resulted in more than 150 million homeless people worldwide, and more than 22 million new people were added to this inventory for climate-related issues. This homeless population has a counterproductive effect on the social, psychological integration efforts by the community and exposure to other severe health-related issues. Methods We systematically identified and reviewed 24 articles which met all three requirements we set forth-i. samples include homeless people, ii. focused on public health-related issues among the same group of people, and iii. used geospatial analysis tools and techniques in conducting the research. Result Our review findings indicated a major disparity in the geographic distribution of the case study locations-all the articles are from six (6) countries-USA (n = 16), Canada (n = 3), UK (n = 2), and one study each from Brazil, Ireland, and South Africa. Majority of the studies used spatial analysis tools to identify the hotspots, clustering and spatial patterns of patient location and distribution. ArcGIS is the most frequently used GIS application, however, studies also used other statistical applications with spatial analysis capabilities. These studies reported relationship between the location of homeless shelters and substance use, discarded needles, different infectious and non-infectious disease clusters. Conclusion Although, most studies were restricted in analyzing and visualizing the trends, patterns, and disease clusters, geospatial analyses techniques can be used to assess health problems such as disease distributions and associated factors across communities. Moreover, health and services and accessibility concerns could be well addressed by integrating spatial analysis into homelessness-related research. This may facilitate policymaking for health-issues among the homeless people and address health inequities in this vulnerable population.
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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 | 0.033 | 0.145 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.037 | 0.036 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| 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".