T126. PSYCHIATRIC PREDICTORS FOR BECOMING HOMELESS AND EXITING HOMELESSNESS: A SYSTEMATIC REVIEW AND META-ANALYSIS
Bibliographic record
Abstract
Abstract Background Homelessness is an increasing societal problem in high-income countries and often linked to psychiatric disorders. However, a study compiling the existing literature is lacking. The aim was to identify individual-level predictors for becoming homeless and exiting homelessness in a systematic review and meta-analysis. Methods We searched PubMed, EMBASE, PsycINFO, and Web of Science (up to January 2018). Becoming homeless and exiting homelessness were the outcomes. Observational studies with comparison groups from high-income countries were included. The Newcastle Ottawa Quality Assessment Scale was used for bias assessment. Random effects models were used to calculate pooled odds ratios (ORs). In all, 116 studies of predictors for becoming homeless and 18 for exiting homelessness were included. Results Psychiatric problems, especially drug use problems (OR 2.9, 95% confidence interval (CI) 1.5–5.1) and suicide attempts (OR 3.6, 95% CI 2.1–6.3) were associated with increased risk of homelessness. However, the heterogeneity was substantial in most analyses (I2>90%), and the estimates should be interpreted cautiously. Adverse life-events, including childhood abuse and foster care experiences, and past incarceration were also important predictors of homelessness. Psychotic problems (95% CI 0.4, 0.2–0.8; I2=0) and drug use problems (OR 0.7, 95% CI 0.6–0.9; I=0) reduced the chances for exiting homelessness. Female sex and having a partner increased the changes of exiting homelessness. Discussion Evidence for several psychiatric predictors for becoming homeless and exiting homelessness was identified. Additionally, socio-demographic factors, adverse life-events, and criminal behavior were important factors. There is a need for more focus on psychiatric vulnerabilities and early intervention to reduce the risk of homelessness.
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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 | 0.013 | 0.032 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.045 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".