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Record W3131622511 · doi:10.32799/ijih.v16i2.33177

Urban Land-Based Healing: A Northern Intervention Strategy

2020· article· en· W3131622511 on OpenAlexvenueaboutno aff
Nicole Redvers, Mélanie Nadeau, Donald Prince

Bibliographic record

VenueInternational Journal of Indigenous Health · 2020
Typearticle
Languageen
FieldHealth Professions
TopicCommunity Health and Development
Canadian institutionsnot available
Fundersnot available
KeywordsIndigenousIntervention (counseling)Environmental planningGeographyFace (sociological concept)PopulationLand useEnvironmental healthMedicineSociologyCivil engineeringEcologyEngineeringNursingSocial science

Abstract

fetched live from OpenAlex

Urban Indigenous populations face significant health and social disparities across Canada. With high rates of homelessness and substance use, there are often few options for urban Indigenous Peoples to access land-based healing programs despite the increasingly known and appreciated benefits. In May 2018, the first urban land-based healing camp opened in Yellowknife, Northwest Territories, Canada, one of the first to our knowledge in Canada or the United States. This camp may serve as a potential model for an Indigenous-led and Indigenous-based healing camp in an urban setting. We present preliminary outcome data from the healing camp in a setting with a high-risk population struggling with substance use and homelessness. Reflections are presented for challenging logistical and methodological considerations for applications elsewhere. This northern effort affords us ample opportunity for expanding the existing knowledge base for land- based healing applied to an urban Indigenous high-risk setting.

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)0.004
metaresearch head score (Gemma)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.045
Threshold uncertainty score0.090

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0090.003
Scholarly communication0.0020.001
Open science0.0020.010
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0130.001

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 head0.099
GPT teacher head0.446
Teacher spread0.347 · 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

Citations21
Published2020
Admission routes2
Has abstractyes

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