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The benefits of information communication technology use by the homeless: a narrative synthesis review

2014· article· en· W1978354124 on OpenAlexaffabout
Adrien Sala, Javier Mignone

Bibliographic record

VenueJournal of Social Distress and the Homeless · 2014
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsNarrativeInformation and Communications TechnologySocial connectednessICTSRelevance (law)Public relationsPsychologySociologySocial psychologyPolitical science

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.730
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0030.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.020
GPT teacher head0.335
Teacher spread0.315 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations16
Published2014
Admission routes2
Has abstractyes

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