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Record W2894718043 · doi:10.1007/s11469-018-9996-3

Land-Based Intervention: a Qualitative Study of the Knowledge and Practices Associated with One Approach to Mental Health in a Cree Community

2018· article· en· W2894718043 on OpenAlexafffundabout
Russ Walsh, David Danto, Jocelyn Sommerfeld

Bibliographic record

VenueInternational Journal of Mental Health and Addiction · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsUniversity of Guelph-Humber
FundersBladder Cancer Advocacy NetworkUniversity of Guelph
KeywordsIndigenousHealth psychologyMental healthFocus groupQualitative researchPsychological interventionTraditional knowledgeIntervention (counseling)SociologyPublic healthIdentity (music)PsychologyPublic relationsMedicineNursingSocial sciencePolitical scienceEcologyPsychotherapistAnthropology

Abstract

fetched live from OpenAlex

This project is a qualitative study of a land-based intervention used in an Indigenous community in northern Ontario. As previous research suggests, a sense of connection to the land is an integral part of Indigenous well-being, and mental health interventions centered around this connection may be more appropriate for use in Indigenous communities than Western treatment approaches that typically emphasize individuality. The present study gains further insight into how a land-based intervention can be applied to an Indigenous community. Interviews with three community members were conducted and summarized in order to understand their views on the background, components, advantages, and challenges of the program. Results showed a focus on strengthening cultural identity, facilitating intergenerational knowledge transfer, and building relationships with others, similar to other land-based programs across Canada. The importance of reconnecting Indigenous youth with their cultural heritage and developing community-centered programs are 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)0.005
metaresearch head score (Gemma)0.007
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 score0.934
Threshold uncertainty score0.132

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0120.010
Scholarly communication0.0020.002
Open science0.0020.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.067
GPT teacher head0.450
Teacher spread0.383 · 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

Citations40
Published2018
Admission routes3
Has abstractyes

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Same venueInternational Journal of Mental Health and AddictionSame topicIndigenous Health, Education, and RightsFrench-language works237,207