Advancing health equity for Indigenous peoples in Canada: development of a patient complexity assessment framework
Bibliographic record
Abstract
BACKGROUND: Indigenous patients often present with complex health needs in clinical settings due to factors rooted in a legacy of colonization. Healthcare systems and providers are not equipped to identify the underlying causes nor enact solutions for this complexity. This study aimed to develop an Indigenous-centered patient complexity assessment framework for urban Indigenous patients in Canada. METHODS: A multi-phased approach was used which was initiated with a review of literature surrounding complexity, followed by interviews with Indigenous patients to embed their lived experiences of complexity, and concluded with a modified e-Delphi consensus building process with a panel of 14 healthcare experts within the field of Indigenous health to identify the domains and concepts contributing to health complexity for inclusion in an Indigenous-centered patient complexity assessment framework. This study details the final phase of the research. RESULTS: A total of 27 concepts spanning 9 domains, including those from biological, social, health literacy, psychological, functioning, healthcare access, adverse life experiences, resilience and culture, and healthcare violence domains were included in the final version of the Indigenous-centered patient complexity assessment framework. CONCLUSIONS: The proposed framework outlines critical components that indicate the presence of health complexity among Indigenous patients. The framework serves as a source of reference for healthcare providers to inform their delivery of care with Indigenous patients. This framework will advance scholarship in patient complexity assessment tools through the addition of domains not commonly seen, as well as extending the application of these tools to potentially mitigate racism experienced by underserved populations such as Indigenous peoples.
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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 | 0.035 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.016 | 0.010 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".