Linking 2006 Census and hospital data in Canada.
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: Record linkage is commonly used in health research to fill data gaps. This study summarizes the linkage of the 2006 Census of Population (excluding Quebec) to hospital data from the Discharge Abstract Database (DAD). DATA AND METHODS: Hierarchical deterministic exact matching was employed to link 2006 Census and DAD (2006/2007, 2007/2008 and 2008/2009) data, based on linkage keys derived from three variables common to both files-date of birth, postal code and sex. The full census file (short-form; 23.4 million) was used for record linkage; the 20% file (long-form; 4.65 million) representing the study cohort was used for validation. Linked files were compared across jurisdictions, years and other selected covariates in terms of eligibility for linkage, keys linked, and linkage and coverage rates. RESULTS: Overall, 80% of linkage keys identified in the DAD were linked to the 2006 Census. The percentage of long-form census respondents linked to at least one hospital record ranged between 5% and 8% across jurisdictions; linkage rates were higher among known high users of hospital services: older age groups, lower-income individuals, and Aboriginal people. In general, the linked census file represents the majority of hospital events that occurred during the study period. Coverage rates (weighted/unweighted) varied by geography and age group, with lower weighted rates for the territories and some younger age groups. INTERPRETATION: With hierarchical deterministic exact matching, census data can be linked to multiple years of DAD data. Incorporation of updated postal codes from tax files reduced linkage rate attrition over time. Lower coverage rates for the territories and younger age groups suggest that these populations may be underrepresented in the linked files.
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 | 0.004 | 0.002 |
| 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 | 0.001 | 0.001 |
| 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