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Record W2891561037 · doi:10.4269/ajtmh.18-0591

To End TB, First-Ever High-Level Meeting on Tuberculosis Must Address Stigma

2018· article· en· W2891561037 on OpenAlex
Amrita Daftary, Ellen M.H. Mitchell, Michael Reid, Endalkachew Fekadu, Eric Goosby

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAmerican Journal of Tropical Medicine and Hygiene · 2018
Typearticle
Languageen
FieldMedicine
TopicTuberculosis Research and Epidemiology
Canadian institutionsMcGill University
FundersNational Institute of Mental Health
KeywordsStigma (botany)TuberculosisSocial stigmaMedicinePublic relationsFamily medicinePsychiatryPolitical scienceHuman immunodeficiency virus (HIV)Pathology

Abstract

fetched live from OpenAlex

World leaders gather to consolidate their commitment to ending tuberculosis (TB). Vital to the success of renewed efforts is an overdue recognition of the pervasive and pernicious influence of TB stigma. TB stigma is sustained in structures, policies, traditions, and norms. Innovative modifications to infection control, drug dispensing, and surveillance practices are required to increase demand for TB screening and effective therapeutic alliances among those diagnosed. The authors argue that reducing TB stigma requires a scientific and inclusive process, with prominent roles for TB survivors and a willingness to integrate and learn from other stigmatized conditions.

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.

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)0.005
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: Empirical
Teacher disagreement score0.612
Threshold uncertainty score0.617

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.048
GPT teacher head0.344
Teacher spread0.297 · 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