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Record W4407802750 · doi:10.1080/0145935x.2025.2468177

Building Social Support into Mental Health and Substance Use Treatment Trajectories: Insights from a Longitudinal Qualitative and Ethnographic Study with Young People Experiencing Unstable Housing and Homelessness

2025· article· en· W4407802750 on OpenAlexafffund
Hella Lee, Madison Thulien, Cameron R. Eekhoudt, Drew Friesen, Daniel Manson, Sarah M. Bagley, Scott E. Hadland, Danya Fast

Bibliographic record

VenueChild & Youth Services · 2025
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsUniversity of British ColumbiaBritish Columbia Centre on Substance UseBC Children's Hospital
FundersNational Institute on Drug AbuseSick Kids FoundationCanadian Institutes of Health ResearchNational Institutes of HealthMichael Smith Health Research BCVancouver Foundation
KeywordsMental healthQualitative researchEthnographySubstance usePsychologyService providerSocial workSubstance abuseSociologyDevelopmental psychologyService (business)PsychiatryEconomic growth

Abstract

fetched live from OpenAlex

This study explores the opportunities and challenges of fostering social support across street-involved young people's mental health and substance use (MHSU) treatment trajectories. We conducted in-depth interviews with youth and their providers between 2017 and 2021. Findings demonstrate that family and others could be crucial catalysts for initiating and re-engaging in MHSU treatment among youth. However, many reported not receiving enough assistance from service providers in navigating complex social relationships with family, friends, and romantic partners. Our findings underscore the need for a more holistic approach to MHSU treatment that encircles the young person and their social networks, including caregivers.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science 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 score0.572
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0040.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.044
GPT teacher head0.390
Teacher spread0.346 · 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

Citations3
Published2025
Admission routes2
Has abstractyes

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