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Record W4392922440 · doi:10.32799/ijih.v19i1.41134

Indigenous Perspectives on Strengths, Resilience, and Well-being

2024· article· en· W4392922440 on OpenAlexvenueno aff
Melissa L. Walls, Nikki Crowe, Vicki Oberstar, Joseph P. Gone, Marcia Kitto, Colleen Bernu, Nicole M. Weiss

Bibliographic record

VenueInternational Journal of Indigenous Health · 2024
Typearticle
Languageen
FieldPsychology
TopicResilience and Mental Health
Canadian institutionsnot available
Fundersnot available
KeywordsResilience (materials science)IndigenousEnvironmental planningEnvironmental resource managementEnvironmental ethicsPolitical scienceGeographyEnvironmental scienceBiologyEcologyPhilosophyPhysics

Abstract

fetched live from OpenAlex

Indigenous communities consistently call for strengths-based, assets-driven approaches to promoting health equity. This includes efforts to expand well-being and resilience frameworks to reflect cultural understandings and perspectives. This study describes community-based participatory research (CBPR) involving focus groups with four diverse groups of Indigenous community members in a single reservation community in the United States. Data were analyzed using inductive and deductive multi-coder processes. Our collaborative efforts led to innovations in planned methods and focal areas of study, including a reframing of “resilience” as one that brings hearts and community together. This approach also yielded a unique, intensive qualitative coding structure that represents a substantive effort to democratize and Indigenize research methods. Community members who participated in focus groups identified Indigenous cultural practices, beliefs, and community as critical components to well-being.

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)0.004
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 score0.021
Threshold uncertainty score0.042

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.013
Scholarly communication0.0030.003
Open science0.0010.005
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.010
GPT teacher head0.394
Teacher spread0.384 · 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

Citations4
Published2024
Admission routes1
Has abstractyes

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