“Call Back When You Are More Homeless:” Missed Targets for Prevention and Risk Reduction Among Youth Who Are Couch Surfing
Bibliographic record
Abstract
Little is known about the health, mental health, and sexual risks experienced by a more hidden population of homeless youth who are solely “couch surfing” (temporarily staying with others) and largely disconnected from services. This paper describes the housing characteristics and risk patterns of this understudied population of homeless youth who are solely couch-surfing and compares their risk patterns to literally homeless youth. A hybrid form of respondent-driven sampling (network-based sampling) was used to recruit 75 homeless young people aged 14-24. A convergent parallel mixed-methods design was used to collect, analyze and interpret the qualitative (interview) and quantitative (survey) data. Quantitative survey data were collected on social support, housing and risk characteristics (mental health, substance use, and sexual risk behaviors). A subset (n=30) of survey respondents participated in a follow-up in-depth interview to further contextualize the risks associated with couch surfing. Non-parametric and parametric methods were performed on survey data to compare characteristics between couch-surfing only and literally homeless youth. A thematic analysis was used to identify common themes in interview transcripts. The qualitative and quantitative data were then compared for convergence and divergence. Results suggest more similarities than differences in risk between couch surfing only and literally homeless youth. No significant differences were found in housing and risk characteristics. Perceived social support was the only significant difference between couch-surfing and literally homeless youth. These findings demonstrate an urgent need for policy reform and specialized resources for couch surfing youth to prevent any further harm among this vulnerable population.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".