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Record W7116756380 · doi:10.1186/s12939-025-02740-8

Building a comprehensive understanding of 2SLGBTQ+ youth homelessness: a scoping review

2025· article· en· W7116756380 on OpenAlexaff
Alex Abramovich, Nicole Elkington, Michael Silberberg, Nelson Pang, Rowen K. Stark, Sarah Bonato

Bibliographic record

VenueInternational Journal for Equity in Health · 2025
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsSt. Michael's HospitalUniversity of TorontoPublic Health OntarioCentre for Addiction and Mental Health
Fundersnot available
KeywordsGrey literatureMental healthChecklistSystematic reviewSocial policyStigma (botany)PsycINFOPublic healthHealth services research

Abstract

fetched live from OpenAlex

Two-spirit, lesbian, gay, bisexual, transgender, queer, questioning, plus additional sexual and gender diverse (2SLGBTQ+) youth are overrepresented among youth experiencing homelessness. 2SLGBTQ+ youth experiencing homelessness are an understudied population, which has resulted in gaps in knowledge about youths’ needs and experiences. The purpose of this scoping review was to synthesize what is currently known about 2SLGBTQ+ youth and young adult homelessness internationally, using a search strategy that included both reviews and grey literature. This review was guided by the five-stage scoping review framework developed by Arksey & O’Malley. We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Review checklist in the reporting of the methodology of this scoping review. The search strategy utilized a three-concept approach to capture research on 2SLGBTQ+ populations, homelessness, and adolescents/youth. Study selection and data extraction was conducted using Covidence Systematic Review Software. A total of 112 sources—including original research articles and review articles—met eligibility criteria and were included in the review. A majority of the articles focused on youths’ experiences with homelessness, highlighting issues such as family violence, conflict, and rejection; physical and sexual victimization; stigma and discrimination in housing programs; mental health and substance use challenges; engagement in survival sex; difficulties accessing and securing employment and education; and exclusion from community and social supports. Articles also discussed the barriers that youth face in trying to access shelter services, including homophobic, biphobic, and transphobic discrimination, and policies and regulations—such as gender-segregated housing—that make services non-gender-affirming. The reviewed articles highlighted strategies for services and programs to effectively address 2SLGBTQ+ youth homelessness, including training staff to be gender-affirming and inclusive, updating policies and data collection practices to be more transgender-inclusive, facilitating community connections, and adapting services to an intersectional framework. The most effective programs were characterized by five novel components: reliance on clinical evidence; trauma-informed approach; provision of safe, stable, and supportive housing; 2SLGBTQ+ peer providers; and opportunities for mutual learning between 2SLGBTQ+ youth and cisgender and heterosexual youth. As a comprehensive review of the existing literature, this scoping review can be leveraged to inform the development and implementation of targeted interventions for 2SLGBTQ+ youth experiencing homelessness across various care settings. The insights presented offer critical information for policymakers, researchers, and professionals seeking to understand and address the unique needs and challenges faced by 2SLGBTQ+ youth experiencing homelessness.

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)0.033
metaresearch head score (Gemma)0.099
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.033
Threshold uncertainty score0.174

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0330.099
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0060.006
Bibliometrics0.0260.019
Science and technology studies0.0020.002
Scholarly communication0.0090.011
Open science0.0030.006
Research integrity0.0050.004
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.358
GPT teacher head0.602
Teacher spread0.244 · 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 designSystematic review
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

Citations3
Published2025
Admission routes1
Has abstractyes

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