Assessment of Tuberculosis Outbreak Definitions for a First Nations On-Reserve Context
Bibliographic record
Abstract
Improving the prevention and control of tuberculosis (TB) in Aboriginal communities in Canada is a matter of great urgency. Canadian-born Aboriginal people account for 21% of TB cases in the country even though they represent only 3.8% of the overall population. Moreover, age standardized rates of TB in Aboriginal people reveal an incidence almost six fold greater than the national rate. There are unique challenges in the prevention and control of TB in First Nations populations. We sought to investigate whether the Canadian Tuberculosis Standards definition being used Canada wide to address TB is appropriate in a First Nations on-reserve context or whether alternate definitions should be considered. In this study, we spoke to health care workers, scientists, and administrators involved in TB programs and care across the country to assess the suitability of the definition used to classify an outbreak. Our data showed that the majority of study participants did not support a First Nations-specific TB outbreak definition. Participants felt that a response protocol would be useful, along with a preamble to the definition detailing unique circumstances that may pertain to an outbreak on-reserve.
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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 | 0.036 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".