Linking 2006 Census and hospital data in Canada.
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.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.025 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".