Area-based methods to calculate hospitalization rates for the foreign-born population in Canada, 2005/2006.
Bibliographic record
Abstract
BACKGROUND: Hospital records lack information about country of birth. This study describes a method for calculating hospitalization rates by the percentage of foreign-born in Census Dissemination Areas (DAs). DATA AND METHODS: Data from the 2006 Census were used to classify DAs by the percentage of the foreign-born population who lived in them. Quintile and tercile thresholds were created to classify DAs as having low to high percentages of foreign-born residents. This information was appended to the 2005/2006 Hospital Morbidity Database via postal codes. Age-sex standardized hospitalization rates were calculated for low to high foreign-born concentration DAs, nationally and subnationally. RESULTS: Nationally, quintile thresholds had better discriminatory power to detect variations in hospitalization rates by foreign-born concentration, but tercile thresholds produced reliable results at subnational levels. All-cause hospitalization rates were lowest among residents of the high foreign-born concentration terciles. Similar gradients emerged in hospitalization rates for heart disease, diseases of the circulatory system, and mental health conditions. The pattern varied more at the subnational level. INTERPRETATION: With this approach, administrative data can be used to calculate hospitalization rates by foreign-born concentration.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".