Two approaches to linking census and hospital data.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: This study compares registry and non-registry approaches to linking 2006 Census of Population data for Manitoba and Ontario to hospital data from the Discharge Abstract Database (DAD). DATA AND METHODS: Using a probabilistic linkage, the registry approach linked the census data to provincial health insurance registries, followed by a deterministic linkage to the DAD based on health insurance number (HIN). The non-registry approach used hierarchical deterministic exact matching based on three variables common to both files to link census data to the DAD. The approaches were compared in terms of linkage and coverage rates, sensitivity and specificity, and consistency of HINs on the linked records. RESULTS: Results of the registry and non-registry linkage approaches were similar. In Manitoba, 7% and 6% of census long-form respondents linked to the DAD with the registry and non-registry linkage approaches, respectively; in Ontario, the linkage rate was 5% for both approaches. With the registry approach, the linked census-DAD data represented 84% (weighted) of hospital admissions in the 2006/2007 DAD in both provinces, compared with 82% in Manitoba and Ontario with the non-registry approach. INTERPRETATION: In the absence of access to provincial health insurance registries with which census data can be linked, a non-registry approach can be used to create a research-quality dataset.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 it