"This is an illness. No one is supposed to be treated badly": Community-based stigma assessments in South Africa to inform TB stigma intervention design
Bibliographic record
Abstract
Abstract Background Though TB-related stigma is a recognized barrier to care, interventions are lacking and gaps remain in understanding the drivers and experiences of TB-related stigma. We undertook community-based mixed methods stigma assessments to inform stigma intervention design. Methods We adapted the Stop TB Partnership stigma assessment tool, and trained three peer research associates (PRAs; two TB survivors, one community health worker) to conduct surveys with people with TB (PWTB, n=93) and caregivers of children with TB (n=24) at peri-urban and rural clinic sites in Khayelitsha, Western Cape, and Hammanskraal, Gauteng Province, South Africa. We descriptively analyzed responses for each stigma experience (anticipated, internal, and enacted), calculated stigma scores, and undertook generalized linear regression analysis. We further conducted 25 in-depth interviews with PWTB (n=22) and caregivers TB (n=3). Using inductive thematic analysis, we performed open coding to identify emergent themes, and selective coding to identify relevant quotes. Themes were organised using the CARD (Constraints, Actions, Risks and Desires) framework. Results Surveys revealed at least one-third of PWTB and one-quarter of caregivers report experiences of anticipated, internal, and/or enacted stigma, which affected engagement throughout the care cascade. Participants in rural locations (compared to peri-urban) reported higher anticipated, internal, and enacted stigma (β-coefficient 0.72, 0.71, and 0.74). Interview participants described how stigma experiences, including HIV intersectional stigma, act individually and in concert as key constraints to impede care, and underpins failure to disclose a TB diagnosis, isolation, and exclusion. Stigma resilience arose through understanding that TB can affect anyone and should not diminish self-worth. Risks of stigma, driven by fears related to disease severity and infectiousness, led to care disengagement and impaired psychological wellbeing. Participants desired counselling, identifying a specific role for TB survivors as peer counsellors, and community education. Conclusions Stigma is highly prevalent and negatively impacts TB care and the well-being of PWTB, warranting its assessment as a primary outcome indicator rather than intermediary contributor to poor cascade outcomes. Multicomponent stigma interventions are needed, including counselling for PWTB and education for health workers and communities. Such interventions must incorporate contextual differences based on gender or setting, and use survivor-guided messaging to foster stigma resilience.
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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 | 0.021 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".