Creation of First Nations Health Profiles Through Data Linkage in Manitoba
Bibliographic record
Abstract
BackgroundFirst Nation peoples (FNs) were not able to identify themselves within administrative datasets due to lack of FNs identifiers, which perpetuates a pan-indigenous approach in advocacy and evaluation capabilities. Linking databases improves the quality and accuracy of FNs health data and offsets the burden of survey fatigue in communities. Creating community profiles helps FNs in identifying health status priorities for communities, Tribal Council and other geographically defined areas.
 MethodsA resolution was passed in September 2017 to link Indian Status Registry (ISR) file with Manitoba Health Registry, with First Nations Health and Social Secretariat of Manitoba (FNHSSM) and Health Information Research Governance Committee (HIRGC) oversight to create a Key Linked file which has First Nations specific information. Encrypted Personal Health Information Numbers (PHINs) were added to the Key Linked file to create a Manitoba First Nations Research file which is linkable to other databases. Information Sharing Agreements (ISA) have been developed with federal and provincial governments to mandate the processes for data linkage.
 ResultsA resolution was passed in September 2017 to link Indian Status Registry (ISR) file with Manitoba Health Registry, with First Nations Health and Social Secretariat of Manitoba (FNHSSM) and Health Information Research Governance Committee (HIRGC) oversight to create a Key Linked file which has First Nations specific information. Encrypted Personal Health Information Numbers (PHINs) were added to the Key Linked file to create a Manitoba First Nations Research file which is linkable to other databases. Information Sharing Agreements (ISA) have been developed with federal and provincial governments to mandate the processes for data linkage.
 ConclusionData Linkage is a key process to assert self-determination, strengthen FNs data governance and achieve Data Sovereignty. Linking databases creates opportunities for FNs to access accurate data that will assist their Nations to lead their own health research and program evaluation that are driven by their own needs and priorities.
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 | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.009 |
| Open science | 0.008 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".