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Record W4411453187 · doi:10.1080/09515070.2025.2521818

Lifting each other up: decolonizing practices for mental health and suicide prevention through Indigenous youth peer support programming

2025· article· en· W4411453187 on OpenAlexaffabout
Shanna Peltier, Jeffrey Ansloos

Bibliographic record

VenueCounselling Psychology Quarterly · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsIndigenousMental healthPeer supportPsychologyPeer groupPsychiatryDevelopmental psychology

Abstract

fetched live from OpenAlex

First Nations, Métis, and Inuit youth in Canada experience significant mental health disparities, with high rates of psychosocial distress and suicide, particularly among those under 45. Over the past 25 years, efforts have aimed to improve psychological services and suicide prevention for Indigenous communities, but risk – based, symptom – focused approaches have failed to address the broader determinants of Indigenous mental health. Decolonizing methodologies emphasize culturally centered, trauma – informed strategies that promote community strength and political self – determination. This study explores the experiences of nine Indigenous youth facilitators engaged in decolonizing mental health initiatives through YouthCO’s Yúusnewas program in British Columbia. In – depth interviews and reflexive thematic analysis reveal three key themes: promoting cultural continuity, fostering self – love through Indigenous knowledge, and youth leadership development. These findings advocate for strengths – based, culturally focused mental health approaches and highlight the need for counselling psychology to embrace Indigenous knowledge and support Indigenous youth.

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)0.004
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.879
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0080.000
Scholarly communication0.0000.000
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 head0.071
GPT teacher head0.449
Teacher spread0.377 · 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

Citations2
Published2025
Admission routes2
Has abstractyes

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