Defining tuberculosis vulnerability based on an adapted social determinants of health framework: a narrative review
Bibliographic record
Abstract
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.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".