Collaborative Data Governance to Support First Nations-Led Overdose Surveillance and Data Analysis in British Columbia, Canada
Bibliographic record
Abstract
First Nations Peoples in the province of British Columbia (BC), Canada, have been disproportionately affected by the overdose crisis. In 2016, a public health emergency was declared by BC’s Provincial Health Officer (PHO) in response to the significant rise in opioid-related overdose deaths reported in BC. New surveillance systems were required to identify trends in overdose events and related deaths in the province as a whole, and for First Nations Peoples. Data sharing and analysis processes that adhered to the principles of OCAP® (ownership, control, access, and possession), and to the Truth and Reconciliation Commission of Canada’s Calls to Action, needed to be developed. The First Nations Health Authority (FNHA), BC Centre for Disease Control, PHO, and the BC Ministry of Health have worked collaboratively to facilitate identification of First Nations persons in surveillance data for appropriate analysis by FNHA. This paper outlines the data stewardship and governance context, principles, and operational considerations for creating overdose surveillance systems to measure overdose events among First Nations Peoples in BC.
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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 | 0.051 | 0.088 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.014 | 0.005 |
| Scholarly communication | 0.013 | 0.004 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".