Implementing healthcare decolonisation for Indigenous people: a systematic review
Bibliographic record
Abstract
BACKGROUND: The Western biomedical system, rooted in colonialism, holds Western science as the only universally valid knowledge system. While it has been justified as an objective approach to improve health, it has failed to address health inequities for Indigenous communities. There is increasing recognition of the need to decolonise healthcare, but its practical application remains unclear. This study systematically reviewed global literature to explore what decolonising healthcare means in practice. METHODS: A systematic search of published and grey literature was conducted across CINAHL, Embase, PubMed, Scopus, Google and reference lists for studies on decolonising health services for Indigenous peoples. Two reviewers independently screened and extracted data from eligible studies. Quality was appraised using the Joanna Briggs Institute's tool for systematic reviews and the Consolidated Criteria for health research involving Indigenous peoples. Data analysis and presentation followed an inductive thematic approach, refined through discussions with authors and external members who identify as Indigenous community members. RESULTS: Fifteen studies from Canada, Australia, Aotearoa (New Zealand), the United States, Chile, and South Africa met the inclusion criteria, all reporting qualitative data. Key elements of decolonising healthcare included community governance, holistic care, relationality and trust, storytelling, reflexive practice, and colonisation-informed care. These were underpinned by cultural, ontological, axiological, and epistemic equity, along with shared power, essential for their decolonial nature. Studies identified barriers and facilitators to decolonising healthcare, reflecting broader structural factors. Reported outcomes included increased patient satisfaction, empowerment, and trust in services. CONCLUSION: Decolonising healthcare requires acknowledging colonialism within healthcare systems and fostering medical encounters with equity between Western and Indigenous ways of knowing, being, and doing. Genuine community-informed partnerships and leadership from Indigenous communities are essential for developing and evaluating services aligned with Indigenous health, well-being, and healing paradigms. REGISTRATION: PROSPERO ID: CRD42024495407.
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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 | 0.019 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".