Would program performance indicators and a nationally coordinated response accelerate the elimination of tuberculosis in Canada?
Bibliographic record
Abstract
Twenty years ago, a National Consensus Conference on Tuberculosis (TB) recommended that the provinces and territories of Canada jointly declare a commitment to TB elimination with national coordination and assured funding, executed by a committee of federal and provincial/territorial representatives. Canada has committed to the global TB elimination targets set forth by the World Health Organization but lacks a coordinated response. In particular, with the exception of one published and implemented by Indigenous Services Canada, there has been no national monitoring and performance framework. Herein, we provide a commentary on the importance, to TB elimination in Canada, of developing such a framework. We invite a debate about whether more can and should be done to monitor and report for action at every jurisdictional level. Of utmost importance will be the need to achieve consensus from stakeholders about what is measured, among whom, how often, who collects and processes data, and how to respond to the successes and failures those data indicate. Insofar, as performance targets are well defined and implemented, national progress towards tuberculosis elimination should accelerate.
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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 | 0.040 | 0.133 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.012 | 0.012 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.007 | 0.004 |
| Research integrity | 0.014 | 0.022 |
| Insufficient payload (model declined to judge) | 0.005 | 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".