Training Indigenous Community Researchers for Community-Based Participatory Ethnographic Dementia Research: A Second-Generation Model
Bibliographic record
Abstract
Conducting community-based participatory research (CBPR) is a complex endeavor, particularly when training non-academic community members. Though examples of CBPR training programs and protocols have been published, they often address a limited set of concepts and are tailored for university or medical school students. Here, we describe the process of developing an online CBPR training program for American Indian (United States) and Indigenous (Canada) community members to conduct multi-sited ethnographic dementia research. This program is unique in its breadth and depth, as our program covers CBPR theory, methods, practical research, and administrative skills. Significantly, this program centers Indigenous methodology, pedagogy, and processes such as two-eyed seeing, storywork, and decolonization approaches. Key to this training program is a "second-generation" approach which incorporates experiential knowledge from prior community-based researchers and academic partners and is designed to develop CBPR capacity among community-based researchers and partnering communities. In this paper, we detail the experience of the first cohort of learners and subsequent improvement of the training materials. Unique challenges related to the specific research focus (dementia care pathway), population/setting (American Indian and Indigenous communities), and technology (rural digital infrastructure) are also discussed.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.069 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".