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Record W4411743863 · doi:10.5588/ijtld.25.0094

Experiences of key populations in multidrug-resistant TB and HIV

2025· article· en· W4411743863 on OpenAlexaff
Hlengiwe Nyilana, Karl Reis, Boitumelo Seepamore, Rubeshan Perumal, Allison Wolf, Karen Guzmán, K. Rivet Amico, Mbawe Zulu, Senzo Hlathi, Xuan Lu, T. Mabuyi, N. Nhlangulela, Régis Perrier, Matthew E. Wilson, Gerald Friedland, Kogieleum Naidoo, Amrita Daftary, Jennifer Zelnick, Martin O’Donnell

Bibliographic record

VenueThe International Journal of Tuberculosis and Lung Disease · 2025
Typearticle
Languageen
FieldMedicine
TopicTuberculosis Research and Epidemiology
Canadian institutionsYork University
FundersNational Institute of Allergy and Infectious Diseases
KeywordsPsychological interventionStigma (botany)PovertyHuman immunodeficiency virus (HIV)Vulnerability (computing)NarrativeSocial stigmaMedicineKey (lock)PsychologyPsychiatryFamily medicinePolitical scienceComputer scienceComputer security

Abstract

fetched live from OpenAlex

Key populations for multidrug-resistant TB (MDR-TB) face heightened vulnerability due to social, behavioral, and structural barriers, such as poverty, substance use and stigma. Here we illustrate the difficulties faced with a composite personal narrative based on the experiences of two participants in a clinical trial of adherence support interventions for people with MDR-TB and HIV. Addressing these factors through tailored interventions is crucial to improving care engagement and challenging one-size-fits-all approaches to TB prevention.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.013
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0130.010
Scholarly communication0.0070.006
Open science0.0020.014
Research integrity0.0040.007
Insufficient payload (model declined to judge)0.0030.000

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 head0.024
GPT teacher head0.363
Teacher spread0.339 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations0
Published2025
Admission routes1
Has abstractyes

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