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Record W2335925726

Disseminating Research in Rural Yup’ik Communities: Challenges and Ethical Considerations in Moving from Discovery to Intervention Development in the Translational Pathway

2013· article· en· W2335925726 on OpenAlexaboutno aff
Inna Rivkin, Joseph E. Trimble, Ellen D. S. López, Samuel G. Johnson, Eliza Orr, James Allen

Bibliographic record

VenueWestern CEDAR (Western Washington University) · 2013
Typearticle
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsnot available
Fundersnot available
KeywordsIntervention (counseling)DisseminationTranslational researchPsychologyEngineering ethicsSociologyMedicinePolitical scienceEngineeringPsychiatry
DOInot available

Abstract

fetched live from OpenAlex

The native people of Alaska have experienced historical trauma and on-going rapid, often externally imposed changes in culture and lifestyle patterns. As a consequence, these populations shoulder a disproportionately high burden of psychological stress. Yup'ik communities in the Yukon Kuskokwim Delta region in Southwest Alaska have experienced epidemics and forced acculturation, contributing to behavioural health issues, including substance abuse and suicide. Cultural loss in Yup'ik communities has resulted in generational gaps that disrupt the transmission of cultural traditions and values important for well-being. Despite these intrusions, Yup'ik communities have retained cultural traditions which act as protective factors against the development of physical and psychological illness. These cultural protective factors can be harnessed to collaboratively develop culturally grounded interventions that reduce stress and build connections across generations, helping communities move towards wellness on their own terms.

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)0.808
metaresearch head score (Gemma)0.712
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: Methods
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.192
Threshold uncertainty score0.237

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.8080.712
Meta-epidemiology (narrow)0.0010.003
Meta-epidemiology (broad)0.0040.003
Bibliometrics0.0050.005
Science and technology studies0.0150.035
Scholarly communication0.0290.020
Open science0.0110.037
Research integrity0.0210.025
Insufficient payload (model declined to judge)0.0070.003

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 head0.476
GPT teacher head0.538
Teacher spread0.063 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designQualitative
DomainMethods
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

Citations0
Published2013
Admission routes1
Has abstractyes

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