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Record W3193406089 · doi:10.1002/jcop.22691

Preventing Indigenous youth homelessness in Canada: A qualitative study on structural challenges and upstream prevention in education

2021· article· en· W3193406089 on OpenAlexaffabout
Jeffrey Ansloos, Amanda Claudìa Wager, Nicole Dunn

Bibliographic record

VenueJournal of Community Psychology · 2021
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsVancouver Island UniversityUniversity of Toronto
Fundersnot available
KeywordsPrecarityThematic analysisIndigenousUpstream (networking)Qualitative researchGeneral partnershipEconomic growthSociologyCriminologyPolitical scienceGender studiesSocial science

Abstract

fetched live from OpenAlex

Drawing on a partnership with a group of Indigenous youth experiencing homelessness in Vancouver, Canada, this study identifies four structural challenges that have impacted them and four actionable upstream strategies to further prevent youth housing precarity. As a secondary analysis of a community-engaged study with youth experiencing homelessness, we conducted a thematic analysis with qualitative data, which included qualitative interviews with five young people and researcher observations. The results reflect the racial, colonial, and economic concerns that impact Indigenous youth experiencing homelessness. The four actionable upstream solutions highlight human rights-based approaches to homelessness, ranging from advancing and strengthening public services, transitional justice processes, and cultural and socioeconomic safety. This study provides strategies to promote Indigenous youth wellbeing and decrease risk of housing precarity, while centering and drawing from youth knowledge production. Strengths and limitations of the study are also discussed.

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)0.004
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.051
Threshold uncertainty score0.370

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.003
Science and technology studies0.0270.008
Scholarly communication0.0040.001
Open science0.0020.005
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.169
GPT teacher head0.517
Teacher spread0.348 · 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 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

Citations19
Published2021
Admission routes2
Has abstractyes

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