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Record W4390193164 · doi:10.1002/alz.071884

Memory Keepers Medical Discovery Team: addressing Indigenous dementia disparities through deep exploration of culture and community‐specific context

2023· article· en· W4390193164 on OpenAlexaboutno aff
Kristen Jacklin, Melissa Blind, Wayne Warry, Dana Ketcher, Jordan Lewis, Collette Pederson, January Johnson, Rhonda Trudeau, Karen Pitawanakwat, Lois Strong, Marlene Summers, Wesley Martin, Nickolas H. Lambrou, Megan Zuelsdorff, Carey E. Gleason

Bibliographic record

VenueAlzheimer s & Dementia · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicIndigenous Health, Education, and Rights
Canadian institutionsnot available
Fundersnot available
KeywordsIndigenousCommunity-based participatory researchHealth equityParticipatory action researchPhotovoiceCommunity engagementContext (archaeology)DementiaFocus groupOutreachSociologyGerontologyMedicinePublic relationsPublic healthPolitical scienceNursingDiseaseEconomic growthGeographyAnthropology

Abstract

fetched live from OpenAlex

Abstract Background The Memory Keepers Medical Discovery Team (MK‐MDT) was established in 2016 to address brain health equity for Indigenous and rural populations. Indigenous populations are under‐represented in Alzheimer’s disease and related dementias (ADRD) research studies and clinical trials, yet there is a dire need to address ADRD disparities in this population. In this paper we outline our research approach and studies underway at the MK‐MDT that are designed to address the ADRD disparity in Indigenous populations in the US and Canada. We highlight our flagship study “Indigenous Cultural Understandings of Alzheimer’s Disease and Dementia – Research and Engagement” (ICARE) as a key foundational ethnographic study necessary to advance the field. Method The MK‐MDT incorporates a community‐based participatory research (CBPR) approach with two‐eyed seeing. Two‐eyed seeing ensures the inclusion and valuing of Indigenous knowledge throughout the research process. In ICARE, we maintain a consistent focus on ethnographic research with Indigenous communities to ensure deep understanding of how culture and context shape the dementia experience. Interviews, sequential focus groups, participant observation and fieldnotes are used to populate an ethnographic database on the lived experience of dementia in Indigenous populations. We incorporate a diversity perspective by working with multiple Indigenous communities for each study. We invest in community engagement infrastructure such as community advisory committees, elder advisors, community‐based researchers and outreach workers to ensure our methods follow local ethical protocols, is culturally safe, and community controlled. Result We have successfully initiated NIH‐funded programs of research aimed at reducing dementia disparities. ICARE was launched in 2018 and included 54 interviews and 14 sequential focus group sessions (n = 17) in four diverse Indigenous communities, establishing the first stage of a robust ethnographic database. Findings support the existence of an Indigenous specific understanding of dementia that impacts the dementia care experience. Conclusion The CBPR approach coupled with two‐eyed seeing has allowed for authentic engagement with multiple Indigenous communities and has supported the collection of ethnographic data that can be used to develop culturally appropriate health promotion materials and interventions. The community‐based infrastructure that has been established is currently supporting multiple projects, including clinical research.

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)0.025
metaresearch head score (Gemma)0.034
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 score0.027
Threshold uncertainty score0.135

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0250.034
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0110.008
Scholarly communication0.0070.007
Open science0.0030.016
Research integrity0.0010.004
Insufficient payload (model declined to judge)0.0040.000

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 head0.069
GPT teacher head0.333
Teacher spread0.264 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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