From Moose Draws to Health Workforce Planning: The Uses of the Provincial Health Insurance Number in New Brunswick
Bibliographic record
Abstract
Poised to receive vital administrative personal health information to serve its role as New Brunswick's seminal research data centre, DataNB (previously NB-IRDT) inadvertently drew attention to a well-established but unsanctioned use of the New Brunswick public health insurance (Medicare) number. DataNB's 2014 request to access the NB Medicare number for research purposes also highlighted its use as a unique identifier for the provincial Moose Draw for hunting licenses, disclosure not granted in legislation. With newfound scrutiny being exercised on the appropriate disclosure and use of personal health information, DataNB's need to access the Medicare number for linking personal administrative data was halted by provincial authorities. In response, provincial government officers and DataNB staff collaborated to develop and introduce legislative mechanisms that would create an authorized Medicare number use for data matching to support research on a wide range of subjects including the recruitment and retention of healthcare professionals in NB.
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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 | 0.009 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.018 | 0.007 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".