A Scoping Review on Access and Use of Technology in Youth Experiencing Homelessness: Implications for Healthcare
Bibliographic record
Abstract
Introduction:Youth are among the fastest growing subset of the homeless population. Youth experiencing homelessness (YEH) face multiple barriers in accessing health information and health care services. As such, they may best be reached through information and communication technologies (ICTs); however, limited efforts have been made to synthesize literature on this topic. In this paper, we review studies on access and use of ICTs among YEH. We also discuss the implications of the review for healthcare. Methods:Using scoping review methodology, we searched four databases (Medline, Embase, PsycInfo, and CINAHL) for studies published between 2005 and 2019, screening 1,927 titles and abstracts. Results:We identified 19 articles reporting on studies with YEH between the ages of 12-30, the majority of which were published in the USA. On average, more than half of the samples owned smartphones, used social media, and accessed the internet weekly to search for housing, employment, health information, and to communicate with family, peers, and health workers; however, many youths faced barriers to sustaining their access to technology. Benefits of using ICTs were connecting with home-based peers, family, and case workers, which was associated with a reduction in substance use, risky sexual health behaviors, and severity of mental health symptoms. Connecting with negative, street-based social ties was identified as the most common risk factor to using ICTs due to its association with engaging in risky sex behaviors and substance abuse. Discussion:This review supports the advancement of research and practice on using ICTs to deliver public health information and health services to YEH, while also considering the health-related risks, benefits, and barriers that YEH face when accessing ICTs.
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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 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.021 | 0.024 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 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".