Fighting tuberculosis hand in hand: A call to engage communities affected by TB as essential partners in research
Bibliographic record
Abstract
Tuberculosis (TB) is an infectious disease closely intertwined with stigma, discrimination, and the social determinants of health. Communities of people affected by TB are experts in their care pathways, but the TB field continues to fall short of meaningfully engaging communities in TB research. This is a missed opportunity to improve the quality, relevance, person-centeredness, positive impact, and sustainability of TB research outputs. We acknowledge the important progress that has been made to date regarding community engagement in TB, but emphasize persisting barriers to meaningful engagement, and the urgent need for updated and comprehensive TB-specific standards for such engagement in research. We highlight that core components of these standards should include the mobilisation of communities affected by TB, bilateral training in community engagement (for researchers and communities), as well as ensuring appropriate remuneration, representation of priority groups, and the use of non-stigmatising language in the engagement process. In addition, to meaningfully incorporate the experiences and expertise of communities affected by TB, their engagement in the research process should occur as early as possible, ideally before research priorities and directions are set, and the scope of the research should encompass questions and outputs relevant to the community. Further, knowledge-sharing between researchers and the community should be ensured, not only of the research outputs but also regarding the engagement process itself, so that lessons learned can be carried forward. Lastly, the sustainability of community engagement processes (whether within institutions or projects) should be ensured, including through adequate funding for such engagement and the training, community mobilisation and relationship-building that this requires.
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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 | 0.245 | 0.246 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.033 | 0.047 |
| Scholarly communication | 0.045 | 0.086 |
| Open science | 0.010 | 0.094 |
| Research integrity | 0.056 | 0.067 |
| Insufficient payload (model declined to judge) | 0.018 | 0.008 |
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".