Unravelling the Complexity of Homelessness: Investigating Reasons and Risk Factors for Chronic Homelessness
Bibliographic record
Abstract
ObjectivesHomelessness is a complex and pressing issue affecting individuals and communities worldwide, including Northern Ireland. Understanding its root causes and associated risk factors is crucial for effective interventions. This study aims to identify primary contributors to homelessness and explore risk factors linked to chronic homelessness in Northern Ireland. MethodsUtilizing an anonymized, linked dataset incorporating Northern Ireland Housing Executive (NIHE) and health and social care data, we will conduct a pioneering record linkage study. This analysis will focus on unravelling homelessness dynamics, particularly chronic homelessness, spanning from 2012 to 2022. ResultsThrough our comprehensive analysis of using linked administrative datasets, we anticipate unveiling significant insights into the prevalence and persistence of chronic homelessness in Northern Ireland. By employing descriptive analyses, we will uncover nuanced patterns and trends, shedding light on the multifaceted dynamics of homelessness within the region. Moreover, our examination aims to identify key factors contributing to the persistence of chronic homelessness, providing crucial insights for developing targeted interventions and support systems. The outcomes of this analysis will not only contribute to a deeper understanding of the complexities surrounding homelessness but also serve as a foundation for evidence-based policymaking and community initiatives aimed at alleviating homelessness in Northern Ireland. ConclusionThis study has the potential to increase our understanding about the characteristics, needs and outcomes of the homeless population in Northern Ireland. It is possible that the results may contribute to facilitating further cross-departmental integration and commitment to addressing vulnerable homeless households with complex needs.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.000 | 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".