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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.020 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".