Digital solutions for adolescent homelessness: a review of health and technological interventions
Bibliographic record
Abstract
Homelessness has profound and far-reaching effects on youth, often resulting in severe physical and mental health challenges. While there is substantial research on the mental health consequences of homelessness among youth, there is a notable gap in the literature concerning the efficacy and availability of current interventions designed to support this vulnerable population. This study aimed to address this gap by providing a comprehensive overview of existing digital interventions for homeless youth, given the rapid evolution of technology. Through a meticulous synthesis of quantitative and qualitative data, the study examined user behaviors, engagement patterns, and attitudinal surveys. Key aspects analyzed included sample sizes, technological solutions, study durations, dropout rates, features of the interventions, their aims, and the resulting outcomes. The analysis indicated that, although the implementation of Technology-Based Interventions (TBI) is a relatively new concept, it is both feasible and promising for future development. As part of this broad TBI, we propose the adoption and implementation of an app that funds the bail of first time homeless youth offenders. The funding is incentivized by providing tax credits to the donors.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".