The benefits of information communication technology use by the homeless: a narrative synthesis review
Bibliographic record
Abstract
Recent studies have suggested that technologies are becoming an increasingly ubiquitous element in the lives of individuals experiencing homelessness. With both Canadian and US researchers reporting staggering levels of homelessness on both sides of the border, an understanding and synthesis of the current literature exploring how technologies are being utilized by homeless individuals and how it may impact their well-being is of relevance to policy makers and social service organizations. The study explored and synthesized literature to examine the ways in which individuals experiencing homelessness utilize information and communication technologies (ICTs), and how the use of ICTs influences the health and social outcomes of individuals experiencing homelessness. The study examined 16 peer reviewed articles using a narrative synthesis systematic review, following three elements of the narrative synthesis approach: preliminary synthesis of findings; exploration of relationships between studies; and assessment of the robustness of the synthesis. In relation to what ICTs are used for by homeless individuals, three major themes emerged: social connectedness, identity management, and instrumental purposes. Furthermore, there was some tentative evidence about a positive relationship between ICT use among individuals experiencing homelessness and health outcomes. The paper discussed limitations, future areas of research, as well as some policy directions.
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 | 0.011 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".