Developing Integrated Healthcare Models for Indigenous People: Insights from a Relational Systematic Scoping Review
Bibliographic record
Abstract
Integrated healthcare models show great promise for addressing health disparities affecting Indigenous people, which are often rooted in the enduring effects of colonisation. These models align with Indigenous holistic views of health, recognizing the importance of community, cultural knowledge, and connection to land. To understand how these models are being developed and implemented, we conducted a systematic scoping review. Guided by Indigenous methodologies and community needs, we searched four databases (Web of Science, PubMed, Scopus and ProQuest) for peer-reviewed literature on integrated healthcare for Indigenous communities in Australia, Canada, the United States, and New Zealand. Included articles were appraised using the Indigenous quality appraisal tool and analysed from a relational perspective supported by the Joanna Briggs Institute's convergent integrated method. Nineteen publications met the inclusion criteria. Most studies were from Australia (53%) and Canada (26%), and most (74%) were published in the last five years, indicating a recent surge in interest. The review identified several key factors critical to the effective implementation of these models. These included strong community leadership and ownership, culturally and contextually relevant approaches, meaningful partnerships with stakeholders, and flexible service delivery. The review further highlights the importance of having motivated and well-trained health providers, as well as adequate funding. The wide variety of methods found in the studies reflects the complexity of integrated care and the influence of distinct cultural, disciplinary and contextual factors. The findings suggest that to improve healthcare and well-being for Indigenous populations, it is crucial to strategically address these key elements.
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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 | 0.095 | 0.211 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.022 | 0.023 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| 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".