Chronic Kidney Disease in Canada's First Nations: Results of an Effective Cross-Cultural Collaboration
Bibliographic record
Abstract
Chronic kidney disease (CKD) is a serious and growing threat to our First Nations peoples' health. Current evidence indicates more rapid progression of CKD in First Nations populations, leading to markedly increased morbidity and mortality. To address this serious health issue, three First Nations communities, Alderville, Hiawatha and Curve Lake, partnered with the Central East Local Health Integration Network (LHIN), the Peterborough Regional Health Center (PRHC) and local family and specialist physicians. A screening tool was developed based on reported best practices to screen all community members over the age of 19 years for CKD. A unique, highly efficacious project algorithm and an electronic database were created to ensure appropriate, seamless patient care and management in this complex, multi-stakeholder environment based on client risk and CKD stage. Over the course of nine months, the project succeeded in screening 83% of all community members over the age of 19 years; 30% of clients were found to have CKD stage two or greater. A strong correlation was found between diabetes and CKD in project participants. A significant number of clients were referred for specialist care. Critical to the success of this project was the effort made to develop collaborative partnerships among five stakeholder groups: the First Nations communities, the LHIN, PRHC, local family physicians and specialist physicians.
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 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 | 0.021 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| 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".