The Role of the Indigenous Patient Navigator: A Scoping Review
Bibliographic record
Abstract
BACKGROUND: Healthcare systems are complex and as a result patients may experience fragmentation of services. Indigenous populations experience increasingly disproportionate health disparities compared to non-Indigenous populations. Patient navigation is known as a patient-centered approach to empower individuals to connect with appropriate services. Literature surrounding the Indigenous Patient Navigator (IPN) remains sparse necessitating this scoping review. Purpose: To map the current state of the role of the IPN internationally within Canada, United States, Australia and New Zealand. METHODS: Estalished methodological framework by Arksey and O'Malley and the PRISMA extension for scoping reviews was used. RESULTS: A total of 820 articles were reviewed from four databases, yielding sixteen articles. CONCLUSIONS: The absence of published literature surrounding the IPN role in Australia and New Zealand was surprising considering similar histories of colonization. The term navigator was used most often and was typically used when describing lay/peer roles. Professional roles were described using specific role descriptions. Six IPN roles were identified including: (1) social service navigation, (2) wholistic support of Indigenous people, (3) advocacy/building capacity, (4) health assessment, (5) administrative navigation, and (6) outreach. Additionally, barriers and enablers IPNs address are identified. This scoping review will assist to promote and reinforce the IPN role.
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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 | 0.016 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.014 | 0.016 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 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".