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Record W4220828080 · doi:10.5206/ijoh.2022.1.13721

Preventing Youth Homelessness in the Context of Covid-19: Complexities and Ways Forward

2022· article· en· W4220828080 on OpenAlexaffvenue
Melissa Perri, Jacqueline Sohn

Bibliographic record

VenueInternational Journal on Homelessness · 2022
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsYork UniversityUniversity of Toronto
Fundersnot available
KeywordsPandemicPsychological interventionContext (archaeology)Equity (law)Stigma (botany)Economic growthCoronavirus disease 2019 (COVID-19)Political sciencePovertyPsychologyCriminologySociologyPublic relationsMedicinePsychiatryGeographyEconomics

Abstract

fetched live from OpenAlex

The coronavirus (COVID-19) pandemic has magnified detrimental social and health experiences and consequences for youth at risk of or experiencing homelessness. Recent research indicates that heightened household tensions due to stay-at-home orders, coupled with pandemic-related financial insecurities, have worsened pre-existing factors for many young people, particularly for those experiencing stigma and violence. As a result, it can be projected that the risk and experience of youth homelessness will intensify. In spite of this, there has been scarce attention to the impacts the current context has on these vulnerable groups. This commentary aims to bring attention to the prevailing issues and challenges faced by youth at-risk of homelessness during the COVID-19 pandemic and provides crucial considerations for preventative solutions by incorporating the Roadmap for the Prevention of Youth Homelessness Framework. Based on research across fields, we offer insights for equity-focused, collaborative interventions, focusing on the education and social services sectors.

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)0.000
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.285
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.103
GPT teacher head0.410
Teacher spread0.307 · 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

Citations1
Published2022
Admission routes2
Has abstractyes

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