Global Climate Implications for Homelessness: A Scoping Review
Bibliographic record
Abstract
Homelessness is a persistent global challenge with significant health impacts on those affected. Homeless people are by definition the most exposed to weather conditions and the social and economic problems caused by extreme weather and climate change and variability. This systematic review was designed to synthesize the academic literature that addresses the health and social implications of global climate change for homelessness. The question examined in this systematic scoping review is the following: What is the current state of knowledge in the scientific literature on the health and social implications of global climate change for homelessness? A systematic scoping review method was used to identify and synthesize the peer-reviewed literature relevant to this question. The databases searched were PsycINFO, Medline, Scopus, and Google Scholar. Of the 26 papers identified in this review, 20 employed original data analyses with conclusions largely inferred from cross-sectional associations. Themes included the potential influence of climate change on homelessness prevalence, climate impacts that exacerbate specific vulnerabilities of homeless populations (e.g., chronic illness, exposure, stigmatization), and health and social outcomes. Service use and design implications were also addressed. Given the scale of the impacts of climate change on homelessness, the literature on this topic poses promising directions but is under-developed in its current state to adequately inform risk mitigation and response planning. A systems framework is proposed here to inform future research and service design.
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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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".