An allied research paradigm for epidemiology research with Indigenous peoples
Bibliographic record
Abstract
BACKGROUND: There is no shortage of epidemiology research describing the ill health of Indigenous peoples in Canada and globally and many of these studies have had negative repercussions on Indigenous communities. However, epidemiology can also be a helpful tool for supporting the health and health services of communities. This paper challenges the reader to consider the harms of epidemiology which essentialize Indigenous communities as sick and in need of help. It then discusses, from the perspective of a settler physician and clinical epidemiology student, how we may be able reconcile the field of epidemiology research with the needs of Indigenous communities. In doing so, it describes an allied research paradigm for epidemiology. RESULTS: Although qualitative research has been substantially informed by critical feminist theories, uptake in quantitative research has been sparser. It is even more rare for Indigenous methodologies to be used to inform quantitative research. This paper is written from a personal perspective, reflecting on the author's prior experiences as well as existing literature on critical feminist theory and Indigenous methodologies, to describe an allied research paradigm. This allied research paradigm follows an ontology that explores the subjectivity within epidemiology and the influence of the positionality of the researcher. It follows an epistemology that understands that knowledge can be generated through many ways including, but not limited to statistical analyses. It follows an axiology that research aims to affect social change and improve the lives of the communities participating in the research. It follows a methodology that is participatory and empowers community partners to meaningfully contribute to statistical research. This allied research paradigm, which makes no claims to universality, describes several important principles: reconciliation, relationships, perspective, positionality, self-determination and accountability. CONCLUSION: Researchers who wish to engage in research in allyship with Indigenous communities must understand the colonial history embedded in health research, commit to a process that honours meaningful relationships with community partners, and carefully consider the implications of their work.
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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 | 0.106 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.018 | 0.100 |
| Scholarly communication | 0.016 | 0.020 |
| Open science | 0.004 | 0.018 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.004 | 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".