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Record W4380249049 · doi:10.1080/17441692.2023.2221729

Defining tuberculosis vulnerability based on an adapted social determinants of health framework: a narrative review

2023· review· en· W4380249049 on OpenAlex
Shishi Wu, Stefan Litvinjenko, Olivia Magwood, Xiaolin Wei

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

VenueGlobal Public Health · 2023
Typereview
Languageen
FieldMedicine
TopicGlobal Maternal and Child Health
Canadian institutionsBruyèreUniversity of OttawaPublic Health OntarioUniversity of Toronto
FundersWorld Health Organization
KeywordsTuberculosisNarrativeVulnerability (computing)Social determinants of healthSocial vulnerabilityMedicineEnvironmental healthSociologyPsychologyPublic healthSocial psychologyComputer scienceNursingPsychological resilienceComputer securityLinguistics

Abstract

fetched live from OpenAlex

The World Health Organization's new End TB Strategy emphasises socioeconomic interventions to reduce access barriers to TB care and address the social determinants of TB. To facilitate developing interventions that align with this strategy, we examined how TB vulnerability and vulnerable populations were defined in literature, with the aim to propose a definition and operational criteria for TB vulnerable populations through social determinants of health and equity perspectives. We searched for documents providing explicit definition of TB vulnerability or list of TB vulnerable populations. Guided by the Commission on the Social Determinants of Health framework, we synthesised the definitions, compiled vulnerable populations, developed a conceptual framework of TB vulnerability, and derived definition and criteria for TB vulnerable populations. We defined TB vulnerable populations as those whose context leads to disadvantaged socioeconomic positions that expose them to systematically higher risks of TB, but having limited access to TB care, thus leading to TB infection or progression to TB disease. We propose that TB vulnerable populations can be determined in three dimensions: disadvantaged socioeconomic position, higher risks of TB infection or progression to disease, and poor access to TB care. Examining TB vulnerability facilitates identification and support of vulnerable populations.

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)0.007
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.753
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0080.001
Bibliometrics0.0000.002
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.140
GPT teacher head0.466
Teacher spread0.326 · 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