ECHO+: Improving access to hepatitis C care within Indigenous communities in Alberta, Canada
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Indigenous populations experience higher rates of hepatitis C virus (HCV) infections in Canada. The Extension for Community Health Outcomes+ (ECHO+) telehealth model was implemented in Alberta to support HCV screening and treatment, using Zoom technology to support Indigenous patient access to specialist care closer to home. Our goal was to expand this program to more Indigenous communities in Alberta, using various Indigenous-led or co-designed methods. METHODS: The ECHO+ team implemented a Two-Eyed Seeing framework, incorporating Indigenous wholistic approaches alongside Western treatment. This approach works with principles of respect, reciprocity, and relationality. The ECHO+ team identified Indigenous-specific challenges, including access to liver specialist care, HCV awareness, stigma, barriers to screening and lack of culturally relevant approaches. RESULTS: Access to HCV care via this program significantly increased HCV antiviral use in the past 5 years. Key lessons learned include Indigenous-led relationship building and development of project outputs in response to community needs influences impact and increases relevant changes increasing access to HCV care. Implementation of ECHO+ was carried out through biweekly telehealth sessions, problem solving in partnership with Indigenous communities, increased HCV awareness, and flexibility resulting from the impacts of COVID-19. CONCLUSION: Improving Indigenous patient lives and reducing inequity requires supporting local primary health care providers to create and sustain integrated HCV prevention, diagnosis, treatment, and support services within a culturally safe and reciprocal model. ECHO+ uses telehealth and culturally appropriate methodology and interventions alongside multiple stakeholder collaborations to improve health outcomes for HCV.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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 it