Using System Dynamics tools to gain insight into intervention options related to the interaction between tobacco and tuberculosis
Bibliographic record
Abstract
Tobacco exposure is an important risk factor for tuberculosis (TB) when considering its effects on population-level disease outcomes. If we hope to gain control over TB globally, we must begin to think 'outside of the box' to identify an extended and multi-faceted intervention strategy that is grounded in an understanding of the particular ways in which key risk factors worsen TB. In light of the role of tobacco exposure as an important, identifiable, modifiable, and preventable risk factor for TB, efforts aiming at reducing tobacco use merit inclusion in such a comprehensive TB control program. The goal of this paper is to share the conceptual framework we have developed using System Dynamics methodology, which diagrams the likely effects of tobacco exposure on TB dynamics in a typical low-income country setting. Using this framework as a guide, we leverage an understanding of the likely mechanisms by which tobacco exposure affects TB risk to systematically explore TB control intervention options. We hope that this paper will help inspire new approaches to extend and enhance traditional TB control efforts. We also hope that the conceptual framework will spark further discussion and research on this important and potentially explosive combination of global public health crises.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".