A population-based study of tuberculosis epidemiology and innovative service delivery in Canada
Bibliographic record
Abstract
OBJECTIVE: To compare and interpret tuberculosis (TB) incidence rates in a Canadian population across two decennials (1989-1998 and 1999-2008) as a benchmark for World Health Organization targets and the long-term goal of TB elimination. The population under study was served by two urban clinics in the first decennial and two urban and one provincial clinic in the second. METHODS: TB rates among Status Indians, Canadian-born 'others' and the foreign-born were estimated using provincial and national databases. Program performance was measured in on-reserve Status Indians in each decennial. RESULTS: In each decennial, the incidence rate in Status Indians and the foreign-born was greater than that in the Canadian-born 'others'; respectively 27.7 and 33.0 times in Status Indians, and 8.0 and 20.9 times in the foreign-born. Between decennials, the rate fell by 56% in Status Indians, 58% in Canadian-born 'others', and 18% in the foreign-born. On-reserve Status Indians had higher rates than off-reserve Status Indians, and the three-clinic model out-performed the two-clinic model among those on-reserve. Rates in the foreign-born varied by World Bank region, and were highest among those from Africa and Asia. CONCLUSION: Status Indians and the foreign-born are at increased risk of TB in Canada. Significant progress towards TB elimination has been made in Status Indians but not in the foreign-born.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".