“As a patient I do not belong to the clinic, I belong to the community.” Co-developing a multi-level, person-centred tuberculosis stigma intervention in Cape Town, South Africa
Bibliographic record
Abstract
Background: Anticipated, internal, and enacted stigma are major barriers to TB care engagement, and directly impact patient well-being. Unfortunately, targeted stigma interventions are lacking. We aimed to co-develop a person-centred stigma intervention with TB-affected community members and health workers in South Africa. Methods: Using a community-based participatory research approach, we conducted ten group discussions with people diagnosed with TB (past or present), caregivers, and health workers (total n=87) in Khayelitsha, Cape Town. Group discussions were facilitated by TB survivors. Discussion guides explored experiences and drivers of stigma and used human-centred design principles to co-develop solutions. Recordings were transcribed, coded, thematically analysed and then further interpreted using the socio-ecological model. Results: Intervention components across socio-ecological levels shared common behaviour change strategies, namely education, empowerment, engagement, and innovation. At the individual level, participants recommended counselling to improve TB knowledge and provide ongoing support. TB survivors can guide messaging to nurture stigma resilience by highlighting that TB can affect anyone and is curable, and provide lived experiences of TB to decrease internal stigma. At the interpersonal level, support clubs and family-centred counselling were suggested to dispel TB-related myths and foster support. At the institutional level, health worker stigma reduction training informed by TB survivor perspectives was recommended. Consideration of how integration of TB/HIV care services may exacerbate TB/HIV intersectional stigma and ideas for restructured service delivery models were suggested to decrease anticipated and enacted stigma. At the community level, participants recommended awareness-raising events led by TB survivors, including TB information in school curricula. At the policy level, solutions focused on reducing the visibility generated by a TB diagnosis and resultant stigma in health facilities and shifting tasks to community health workers. Conclusions: Decreasing TB stigma requires a multi-level approach. Co-developing a person-centred intervention with affected communities is feasible and generates stigma intervention components that are directed and implementable. Such community-informed intervention components should be prioritised by TB programs, including integrated TB/HIV care services.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".