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Record W4221094755 · doi:10.1177/21568693221082206

Health System Access for Precariously Housed Youth: A Participatory Youth Research Project

2022· article· en· W4221094755 on OpenAlexaff
Naomi Nichols, Jayne Malenfant

Bibliographic record

VenueSociety and Mental Health · 2022
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsConcordia UniversityTrent University
Fundersnot available
KeywordsMental healthAutonomyParticipatory action researchQualitative researchYouth participationPsychologyHealth promotionYouth engagementCitizen journalismPublic relationsSociologyNursingPolitical scienceMedicinePublic healthPsychiatry

Abstract

fetched live from OpenAlex

This article builds from young people’s experiences navigating health system organizations to identify concrete institutional and policy processes that pose problems for youth experiencing or at risk of homelessness, as well as those that show promise in terms of health promotion. Our participatory youth research team explored homeless youth’s health-seeking practices, the specific barriers they face, and relations between their health-seeking efforts and their homelessness. Youth and adult co-researchers interviewed 38 individual youth (aged 16–29) who completed 64 qualitative institutional history interviews. Their accounts illuminate a systemic lack of capacity to address the mental health needs of homeless youth. Unable to secure access to timely, sufficient, and suitable programs and services, youth navigate patchwork of crisis and emergency supports that undermine their autonomy, their health, and their housing stability. Finally, youth without stable housing—particularly those who are Trans* and non-binary youth and/or who use drugs—report discrimination in health care settings. Our results suggest that access to timely, appropriate, de-stigmatizing, and consistent (mental) health services is one way to prevent youth 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)0.007
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 score0.030
Threshold uncertainty score0.989

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0120.000
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.424
GPT teacher head0.562
Teacher spread0.138 · 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

Citations8
Published2022
Admission routes1
Has abstractyes

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