Decolonizing Public Healthcare Systems: Designing with Indigenous Peoples
Bibliographic record
Abstract
Indigenous peoples around the world are being failed by current public health systems. This is directly linked to the ongoing issue of colonialism. We need to decolonize health systems if we want to improve healthcare provision for indigenous peoples. In this article, I explore how the confluence of increasingly expansive thinking in the fields of health and design may be used to provide a space for the process of decolonization to occur. I use postcolonial theory to make visible ways in which Indigenous peoples have been ignored in current approaches to healthcare provision. In this article I use two case studies of work undertaken with Indigenous communities. I present some initial evidence from these sites to demonstrate how we engaged a design process that served to decolonize the relevant health systems. Using the spaces created in these processes has helped change systems of healthcare, in these two regions, to become more responsive to the actual needs and lifeworlds of these two Indigenous groups as they see themselves rather than as they may be seen by others. This is a process of decolonization. In this article, I have put forward some general ideas and frames of reference about decolonizing our healthcare systems through design that may be able to be expanded on and utilized by others working with Indigenous peoples or other groups impacted by the colonial process. • Public health systems around the world are failing the needs of Indigenous peoples. • Rebuilding health systems that deliver appropriate care to all requires a process of decolonization. • Design offers pathways forward to support the decolonization of public healthcare systems.
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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 | 0.061 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.017 | 0.048 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".