Failing Siracusa: governments' obligations to find the least restrictive options for tuberculosis control
Bibliographic record
Abstract
One of the world's leading causes of death, tuberculosis (TB) remains a stigmatized and feared disease. Prevention, diagnosis, and adherence to TB treatment remain a challenge for many people, including migrants, those with alcohol and drug dependency, sex workers, people living with the human immunodeficiency virus, and individuals with disabilities. Low levels of TB treatment literacy and ignorance of transmission risks are common, and-along with inadequate funding for treatment support-contribute to patients' non-adherence to treatment. Recent cases involving the detention of individuals with TB in Kenyan and Canadian correctional facilities illustrate the circumstances under which individuals interrupt treatment and how health authorities seek restrictive measures to oversee and compel treatment. The legitimacy of restrictive measures is often defended by international public health authorities in relation to the non-binding Siracusa Principles. Yet in practice, as illustrated by examples from Kenya and Canada, government authorities and local laws sometimes do not fully meet, or entirely disregard, the requirements in the Siracusa Principles that restrictions on rights in the name of public health be strictly necessary and the least intrusive available to reach their objective. In addition, more specific standards are required at the international level to guide states' development and use of rights-restricting measures to address TB.
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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 | 0.072 | 0.097 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.043 |
| Scholarly communication | 0.014 | 0.009 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.015 | 0.022 |
| Insufficient payload (model declined to judge) | 0.003 | 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".