MétaCan
Menu
Back to cohort
Record W2571291993

Causes of Child and Youth Homelessness in Developed and Developing Countries: A Systematic Review and Meta-analysis

2016· review· en· W2571291993 on OpenAlexafffund
Lonnie Embleton, Hana Lee, Jayleen K. L. Gunn, David Ayuku, Paula Braitstein

Bibliographic record

VenueIUScholarWorks (Indiana University) · 2016
Typereview
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsUniversity of Toronto
FundersEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNational Institute of Child Health and Human DevelopmentCanadian Institutes of Health ResearchNational Institutes of Health
KeywordsChild healthPolitical scienceHuman development (humanity)Public healthPsychologyMedicineNursingFamily medicineLaw
DOInot available

Abstract

fetched live from OpenAlex

IMPORTANCE: A systematic compilation of children and youth's reported reasons for street involvement is lacking. Without empirical data on these reasons, the policies developed or implemented to mitigate street involvement are not responsive to the needs of these children and youth. \nOBJECTIVE: To systematically analyze the self-reported reasons why children and youth around the world become street-involved and to analyze the available data by level of human development, geographic region, and sex. \nDATA SOURCES: Electronic searches of Scopus, PsychINFO, EMBASE, POPLINE, PubMed, ERIC, and the Social Sciences Citation Index were conducted from January 1, 1990, to the third week of July 2013. We searched the peer-reviewed literature for studies that reported quantitative reasons for street involvement. The following broad search strategy was used to search the databases: "street children" OR "street youth" OR "homeless youth" OR "homeless children" OR "runaway children" OR "runaway youth" or "homeless persons." \nSTUDY SELECTION: Studies were included if they met the following inclusion criteria: (1) participants were 24 years of age or younger, (2) participants met our definition of street-connected children and youth, and (3) the quantitative reasons for street involvement were reported. We reviewed 318 full texts and identified 49 eligible studies. \nDATA EXTRACTION AND SYNTHESIS: Data were extracted by 2 independent reviewers. We fit logistic mixed-effects models to estimate the pooled prevalence of each reason and to estimate subgroup pooled prevalence by development level or geographic region. The meta-analysis was conducted from February to August 2015. \nMAIN OUTCOMES AND MEASURES: We created the following categories based on the reported reasons in the literature: poverty, abuse, family conflict, delinquency, psychosocial health, and other. \nRESULTS: In total, there were 13 559 participants from 24 countries, of which 21 represented developing countries. The most commonly reported reason for street involvement was poverty, with a pooled-prevalence estimate of 39% (95% CI, 29%-51%). Forty-seven studies included in this review reported family conflict as the reason for street involvement, with a pooled prevalence of 32% (95% CI, 26%-39%). Abuse was equally reported in developing and developed countries as the reason for street involvement, with a pooled prevalence of 26% (95% CI, 18%-35%). Delinquency was the least frequently cited reason overall, with a pooled prevalence of 10% (95% CI, 5%-20%). \nCONCLUSIONS AND RELEVANCE: The street-connected children and youth who provided reasons for their street involvement infrequently identified delinquent behaviors for their circumstances and highlighted the role of poverty as a driving factor. They require support and protection, and governments globally are called on to reduce the socioeconomic inequities that cause children and youth to turn to the streets in the first place, in all regions of the world.

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 imitation

Not 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.

metaresearch head score (Codex)0.029
metaresearch head score (Gemma)0.070
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.029
Threshold uncertainty score0.152

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0290.070
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0170.026
Bibliometrics0.0120.014
Science and technology studies0.0010.001
Scholarly communication0.0050.003
Open science0.0030.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0050.000

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 head0.104
GPT teacher head0.379
Teacher spread0.275 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designMeta-analysis
Domainnot available
GenreReview

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

Citations10
Published2016
Admission routes2
Has abstractyes

Explore more

Same venueIUScholarWorks (Indiana University)Same topicHomelessness and Social IssuesFrench-language works237,207