A Review of the Impact of Homelessness on Mental Health
Bibliographic record
Abstract
Background: Homelessness has become a complex issue with profound impacts on society. Social determinants like housing significantly impacts human well-being in numerous ways, ranging from physical safety to appropriate access to necessities such as healthy food options and medical care. This research seeks to delve deeper into understanding how being homeless can affect mental health outcomes. Methodology: A literature review conducted following a systematic method was integral to our research process. We searched PubMed, PsycINFO, and Scopus, utilizing a combination of keywords related to homelessness, mental health, and their impact. Results: The reviewed studies consistently highlighted the prevalence of mental health disorders among homeless individuals, ranging from depression, suicide, alcoholism, substance abuse, and Schizophrenia. The evidence highlights the complex relationship between homeless status and psychological well-being, noting that lack of secure housing can trigger and exacerbate mental illness. Conclusion: This review emphasizes the significance of providing homeless individuals with essential mental health aid and secure housing accommodations cannot be underscored enough. By recognizing the relationship between homelessness and mental health, society can work towards implementing effective strategies that promote recovery and social inclusion for this vulnerable population.
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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".