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Record W4401695240 · doi:10.3390/youth4030077

Unveiling the Pathways: Mapping and Understanding Hidden Homelessness Among 2SLGBTQ+ Youth in Ontario

2024· article· en· W4401695240 on OpenAlexaffabout
Katie MacEntee, Nicole Elkington, John Segui, Alex Abramovich

Bibliographic record

VenueYouth · 2024
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsCentre for Addiction and Mental HealthPublic Health OntarioUniversity of Toronto
Fundersnot available
KeywordsSociologyCriminologyPolitical sciencePsychology

Abstract

fetched live from OpenAlex

Transphobic and homophobic violence and discrimination within homes and housing programs lead many 2-Spirit, lesbian, gay, bisexual, transgender, and queer (2SLGBTQ+) youth to find alternative, temporary, and insecure housing. These types of living situations are considered “hidden homelessness”. This study interviewed 2SLGBTQ+ youth (n = 6) and key informants (n = 12) who have experienced and/or who support hidden homelessness across three sites in Ontario (Toronto, York Region, and London). The results suggest experiences of hidden homelessness for 2SLGBTQ+ youth are nonlinear, with pathways driven by family conflict combined with the high cost of living and lack of employment, making independent living unaffordable. Additionally, youth avoid services where they experience discrimination and often experience social isolation. In rural and suburban areas, youth have fewer options for safe and inclusive services. When services are accessible, wrap-around supports that address the complexity of their situations help youth exit hidden homelessness. Prevention strategies should focus on addressing family conflict and supporting reunification when it is safe to do so. For those who are unable to return home, there is a need for increased financial support and more affordable housing. Building comprehensive population-based support services is recommended to address the overrepresentation of 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 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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.520
Threshold uncertainty score0.899

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.190
GPT teacher head0.343
Teacher spread0.153 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations3
Published2024
Admission routes2
Has abstractyes

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