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Record W2312796089 · doi:10.5588/pha.12.0094

Failing Siracusa: governments' obligations to find the least restrictive options for tuberculosis control

2013· article· en· W2312796089 on OpenAlexaboutno aff
Katherine Todrys, Eric C. Howe, Joseph J Amon

Bibliographic record

VenuePublic Health Action · 2013
Typearticle
Languageen
FieldMedicine
TopicTuberculosis Research and Epidemiology
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineTuberculosisPublic healthIgnoranceLegitimacyGovernment (linguistics)CriminologyLawPolitical scienceNursingPathology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.751
Threshold uncertainty score0.530

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.113
GPT teacher head0.401
Teacher spread0.288 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations43
Published2013
Admission routes1
Has abstractyes

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