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Record W2947268035 · doi:10.32799/ijih.v14i1.31939

I’taamohkanoohsin (everyone comes together): (Re)connecting Indigenous people experiencing homelessness and addiction to their Blackfoot ways of knowing.

2019· article· en· W2947268035 on OpenAlexaffvenueabout
Janice Victor, Melissa Shouting, Chelsey DeGroot, Les Vonkeman, Mark Brave Rock, Roger Hunt

Bibliographic record

VenueInternational Journal of Indigenous Health · 2019
Typearticle
Languageen
FieldHealth Professions
TopicCommunity Health and Development
Canadian institutionsLethbridge CollegeUniversity of Lethbridge
Fundersnot available
KeywordsGrassrootsIndigenousAddictionPopulationAttendanceDowntownCriminologySociologyPsychologyPolitical scienceGeographyPsychiatryLawPolitics

Abstract

fetched live from OpenAlex

Addiction and homelessness are closely related outcomes for many Indigenous Canadians who live with extensive intergenerational trauma caused by residential school and the 60s Scoop. In recent years, the rise of opioid addiction along with related overdoses and mortalities in many parts of Canada has led to what is being called an opioid crisis. (Re)connection to Indigenous ways of knowing and practices are frequently seen as a path to healing; therefore, an innovative grassroots program was developed recently in a southern Alberta city to address addictions and homelessness within a largely Blackfoot population. The program increased access to traditional cultural resources and activities in a visible, downtown location to a population who are among the most marginalized in society. A Two-Eyed Seeing framework was used perform a program evaluation and analyze participant and key informant interviews. The results indicated that attendance connected people with their spirits, inspiring strength and hope for the future, and ameliorated spiritual homelessness. The program formed a safe space where relationships were strengthened, people felt respected, and meaningful activity away from substances was available.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.314
Threshold uncertainty score0.858

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.049
GPT teacher head0.373
Teacher spread0.325 · 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.

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

Citations7
Published2019
Admission routes3
Has abstractyes

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