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Record W4283788353 · doi:10.18584/iipj.2022.13.1.10928

Indigenous Engagement in Health Research in Circumpolar Countries: An Analysis of Existing Ethical Guidelines

2022· article· en· W4283788353 on OpenAlexafffundvenueabout
Josée G. Lavoie, Jon Petter Stoor, Katie Cueva, Gwen Healey Akearok, Elizabeth Rink, Christina Viskum Lytken Larsen, Елена Гладун

Bibliographic record

VenueInternational Indigenous Policy Journal · 2022
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsQaujigiartiit Health Research CentreUniversity of Manitoba
FundersCanadian Institutes of Health ResearchNorthwestern University
KeywordsIndigenousCircumpolar starGeneral partnershipCommunity engagementPolitical scienceArcticStakeholder engagementChecklistLegislationGeographyPublic relationsPsychologyLawEcology

Abstract

fetched live from OpenAlex

In this paper, we review existing ethical guidelines that support Circumpolar Indigenous Peoples’ engagement in health research. For this study, we collated national and regional ethical guidelines addressing health research engaging with Indigenous communities. Our study found that ethical guidelines addressing Indigenous engagement in health research have emerged in Canada and the U.S.A. Currently, there are no Indigenous-specific provisions in national guidelines, or legislation concerning health research engaging Indigenous peoples, in Denmark, Finland, Greenland, Norway, Sweden, or Russia. Where guidelines exist, they show considerable variations. We conclude that guidelines are essential to ensure that research undertaken in Indigenous communities is relevant and beneficial to those communities, is conducted respectfully, and that results are appropriately contextualized and accurate. We believe that our analysis might serve as a checklist to support the development of comprehensive guidelines developed by, or at least in partnership with, Arctic Indigenous communities.

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)0.036
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.184
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0360.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0060.003
Science and technology studies0.0090.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.006
Insufficient payload (model declined to judge)0.0010.000

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 head0.480
GPT teacher head0.616
Teacher spread0.136 · 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.

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

Citations8
Published2022
Admission routes4
Has abstractyes

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