Administrative data and the manitoba centre for health policy: some reflections.
Bibliographic record
Abstract
The authors review their 30 years' experience in determining the best research applications for routinely collected data from ministries of health, education and social services. They describe the rich research opportunities afforded by 40 years of data on health - i.e., every patient contact with hospitals, physicians, drugs and more - from the problems encountered in convincing an academic journal that meaningful findings could be culled from information collected on paying bills and tracking patients, through studies on education (enrolment, grades, standardized tests for grades 1 to 12), family characteristics (residential moves, marital formation and breakdown, number and timing of births) and social services (welfare recipients, children taken into care, protection services offered children in the family). They also detail how and why the Manitoba Centre for Health Policy was founded, and how it has continued through multiple ministerial, deputy and government changes.
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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 | 0.173 | 0.202 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.015 |
| Science and technology studies | 0.011 | 0.025 |
| Scholarly communication | 0.028 | 0.018 |
| Open science | 0.008 | 0.010 |
| Research integrity | 0.019 | 0.028 |
| Insufficient payload (model declined to judge) | 0.009 | 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".