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Record W4312086886 · doi:10.1002/alz.067158

Developing an Ethnographic Community‐Based Participatory Research Dementia Study with Diverse Indigenous Populations: Indigenous Cultural understandings of Alzheimer’s disease and related dementias – Research and Engagement (ICARE) project

2022· article· en· W4312086886 on OpenAlexaboutno aff
Melissa Blind, Wayne Warry, Nickolas H. Lambrou, Karen Pitawanakwat, Dana Ketcher, Collette Pederson, January Johnson, Annamarie Hill, Melinda Dertinger, Jessica Koski, Rhonda Trudeau, Lois Strong, Marlene Summers, Wesley Martin, Jordan Lewis, Megan Zuelsdorff, Carey E. Gleason, Kristen Jacklin

Bibliographic record

VenueAlzheimer s & Dementia · 2022
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsnot available
Fundersnot available
KeywordsIndigenousParticipatory action researchCommunity-based participatory researchDementiaHealth equityPhotovoiceCommunity engagementGerontologyFocus groupSociologyMedicinePolitical scienceDiseasePublic healthEconomic growthPublic relationsNursingAnthropology

Abstract

fetched live from OpenAlex

Abstract Background Dementia diagnoses have been increasing in Indigenous populations for over a decade and have now surpassed rates in non‐Indigenous populations in both Canada and the U.S. Prevalence rates of Alzheimer’s Disease and Related Dementias (ADRD) are approximately 3 times higher in Indigenous populations compared to White populations, with a 10‐year earlier onset . The ICARE project (NIH –5R56‐AG‐62307‐2) qualitatively explores the impact of ADRD in Indigenous populations across four diverse sites in Canada and the United States. Method We used a community‐based participatory research (CBPR) approach and a two‐eyed seeing framework to establish research partnerships with four Indigenous communities/regions: 7 Anishinaabe First Nations on Manitoulin Island, Ontario Canada; Red Lake Tribal Nation, Minnesota; Grand Portage Tribal Nation, MN; and the Oneida Nation, Wisconsin. The community engagement strategy supported the infrastructure needed to conduct dementia research in a culturally safe and appropriate way that respects Tribal Sovereignty. Result Indigenous community‐based researchers were hired at each site and each underwent research training designed by the ICARE team. With guidance from local leadership, we established 8–12‐member community advisory groups at each site to serve as partners to guide the research. We collected preliminary ethnographic data on social determinants of health, demographics, community health, aging and dementia specific services. We demonstrated feasibility with pilot data collection of key informant interviews with Traditional Knowledge Keepers, Administrators, and Providers (n = 54) and sequential focus groups with local health care staff and formal caregivers that work with Indigenous older adults (n = 17). Conclusion NIH/NIA R56 funding resulted in the establishment of the community‐based infrastructure to support on‐going ADRD research in the four diverse communities. We demonstrated the feasibility of supporting community‐engaged dementia research partnerships with Indigenous communities who have differing cultural, political and historical contexts. This foundation is supporting the expansion of ICARE into a 5‐year ethnographic research study aimed at creating a foundational ethnographic database of AI/FN lived experience of ADRD across the disease trajectory that can be examined to inform the creation of culturally appropriate and safe approaches to improve dementia diagnostics, care, and outreach (NIH‐R01‐AG‐62307‐2).

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)0.021
metaresearch head score (Gemma)0.010
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 score0.082
Threshold uncertainty score0.163

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0210.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0120.004
Scholarly communication0.0030.002
Open science0.0020.007
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 head0.637
GPT teacher head0.520
Teacher spread0.117 · 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

Citations1
Published2022
Admission routes1
Has abstractyes

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