“One-by-One, TB Took Everything Away From Me”: A Photovoice Exploration of Stigma in Women with Drug-Resistant Tuberculosis in Mumbai
Bibliographic record
Abstract
Stigma related to drug-resistant tuberculosis (DR-TB), one of the world's most severe infectious diseases, is a major barrier to TB elimination particularly for women living in settings of gender inequity. Drawing on the participatory action research (PAR) framework of photovoice, we explored lived experiences of DR-TB stigma among nine affected women in Mumbai, India. Consenting women took, shared, and contributed to the critical interpretation of 37 non-identifying images and associated narratives with one another and with PAR researchers. The study surfaced vivid, untold stories of trauma and life-altering encounters with enacted, anticipated, and internal stigma, that were characterized by loss (of self, voice, status, mobility), abuse (mental, social) and deep internal distress (shame, isolation, suffocation, peril). The study also revealed how stigmatized women found means to build resilience and resist the impacts of stigma. We further witnessed the building of their collective resilience through study participation. Photovoice proved to be a uniquely compelling method of data capture and interpretation, with potential to develop meaningful engagement and solidarity among women affected by DR-TB.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.016 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".