YOUTH HOMELESSNESS IN CROATIA: PATHWAYS, CHALLENGES, AND SUPPORT SYSTEMS
Bibliographic record
Abstract
Youth homelessness is a complex issue. The experience of living in alternative care, combined with a lack of support, is a key risk factor that increases the likelihood of youth becoming homeless. This study aimed to explore the lives of homeless youth through their personal experiences, focusing on their pathways to homelessness and the support they would have required to avoid it. Qualitative research was conducted with six young people aged 15 to 29 who were homeless and residing in a shelter in Zagreb. Data were collected through semi-structured interviews and analyzed using a thematic analysis approach. The results showed that the participants all had a background in alternative care, often with multiple placements while in care. Key factors contributing to homelessness included aging out of the care system, broken family relationships, mental health issues, and unemployment. The findings showed that these young homeless individuals primarily relied on formal support systems. They emphasized the need for practical assistance, access to social welfare assistance, professional support and empowerment, and emotional support from trusted individuals. This research underscores the importance of providing targeted, systematic, support to help young homeless people overcome the challenges they face on their path to a stable and independent life.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".