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Mental Health Promotion With Aboriginal Youth

2017· book-chapter· en· W4236155141 on OpenAlexaboutno aff
Claire V. Crooks, Caely Dunlop

Bibliographic record

VenueOxford University Press eBooks · 2017
Typebook-chapter
Languageen
FieldHealth Professions
TopicCommunity Health and Development
Canadian institutionsnot available
Fundersnot available
KeywordsMental healthSocial connectednessPromotion (chess)Public relationsIntervention (counseling)Identity (music)Culturally appropriatePolitical sciencePositive Youth DevelopmentHealth promotionPsychologySociologySocial psychologyMedicineDevelopmental psychologyGerontologyNursingPublic healthPsychotherapistPsychiatry

Abstract

fetched live from OpenAlex

Aboriginal youth in Canada are at disproportionate risk for a range of mental health concerns compared to their non-Aboriginal counterparts. To address this disparity, communities, researchers and policymakers have called for culturally relevant prevention and intervention programming to mitigate risk and promote well-being. A number of promising initiatives have been developed that are grounded in culture. The goal of these programs is to maximize the protective influence of multiple facets of culture in youth’s lives, such as cultural identity, connectedness, and engagement in traditional practices. One such program is The Fourth R: Uniting Our Nations, a strengths-based, culturally relevant program delivered to Aboriginal youth in Canadian schools. This chapter outlines the rationale for promoting such programming with Aboriginal youth. We describe the development and evaluation of the Uniting Our Nations program. We also highlight the importance of authentic partnerships and committing to a time frame that is sufficient for this work.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.963
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
Bibliometrics0.0000.000
Science and technology studies0.0040.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
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 head0.091
GPT teacher head0.354
Teacher spread0.263 · 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 designNot applicable
Domainnot available
GenreOther

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
Published2017
Admission routes1
Has abstractyes

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