Beyond Four Walls: Understanding the Lived Experiences of Homelessness in Kingston Through a Review of Literature
Bibliographic record
Abstract
Homelessness is a significant issue in Kingston, Ontario, with a 2018 report estimating that 1,725 people experienced homelessness in the city over the course of the year. This literature review aims to synthesize existing research on the prevalence, causes, and interventions of homelessness in Kingston, in order to identify gaps in knowledge and suggest future research and policy directions. A systematic search of electronic databases was conducted, with inclusion criteria focusing on articles that explore the prevalence, causes, and interventions of homelessness in Kingston. The lack of affordable housing was identified as a major factor contributing to homelessness in Kingston, along with inadequate income and employment opportunities, mental health and addiction issues, domestic violence, and a lack of supportive services. Several programs, such as the Kingston HomeBase Housing program and the Street Health Centre, have been found to be effective in addressing immediate needs, but long-term outcomes of these interventions require more research. Additionally, barriers to accessing and implementing interventions, such as a lack of affordable housing and funding for support services, and stigma and discrimination against those experiencing homelessness, require further attention. Overall, there is a need for continued research on specific sub-populations and the long-term outcomes of interventions, as well as continued investment in evidence-based interventions and programs aimed at addressing homelessness in Kingston.
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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".