Two-Eyed Seeing in action: Project extension for community health outcomes – Indigenous chronic pain & substance use
Bibliographic record
Abstract
Background: Indigenous Peoples in Canada experience health disparities, including higher rates of chronic pain. Many report distrust of the health system due to factors such as racial discrimination. A lack of appreciation and respect for Indigenous knowledges further contributes to feelings of alienation. In 2022-2023, we offered the first Project Extension for Community Healthcare Outcomes (Project ECHO) Indigenous Chronic Pain and Substance Use Health (ICP&SU) to health care providers interested in improving chronic pain care with and for Indigenous Peoples in Canada. The program reflects a Two-Eyed Seeing approach weaving together Indigenous and Western approaches to chronic pain and substance use health care. Aims: We describe the development and implementation of Project ECHO ICP&SU. Methods: Following guidance from the project Elder, we use storytelling, centered around the metaphor of weaving, to discuss the conception and implementation of Project ECHO ICP&SU. We also describe our engagement in sharing circles and ceremonies to share stories, knowledges, and lessons learned. Results: With strong Anishinaabe leadership, the program was implemented as intended and reached 121 health care professionals. Lessons learned included an overt recognition of the influence of different structures and institutions on programs and for a culturally safer development and evaluation frameworks for future Project ECHOs to improve care with and for Indigenous Peoples. Conclusions: Project ECHO can be a vehicle to enact Truth and Reconciliation Calls to Action through weaving relationships and knowledges to create culturally safer institutions and practices to improve chronic pain, substance use health, and wellness, with and for 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 | 0.019 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.007 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.019 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.016 | 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".