When prevention is dangerous: perceptions of isoniazid preventive therapy in KwaZulu-Natal, South Africa
Bibliographic record
Abstract
Setting: In 2011, the South African government began to offer isoniazid preventive therapy (IPT) through the public health system to presumptively treat latent tuberculous infection (LTBI) among people living with human immunodeficiency virus. Objective: To describe IPT perceptions and experiences in three Zulu communities in KwaZulu-Natal Province, South Africa. Design: Using a combination of community-based research and ethnographic methods, we undertook 17 individual and group interviews between October 2014 and May 2015. Interviews transcripts were analysed using qualitative content analysis and validated with grass-roots community advisors. Results: Participants reported multiple ways in which IPT was perceived as dangerous: when costs related to pill collection or consumption were unsustainable, or when daily pill consumption resulted in stigma or was seen to introduce excess dirt or toxins, ‘ ukungcola ’, in the body. Theories on dirt are evoked to describe how IPT was perceived as ‘matter out of place’ when given to people who believed themselves to be healthy, suggesting that under the current TB aetiological model in Zulu culture, ‘prevention as tablet’ may not fit. Conclusion: Implementing IPT without understanding the realities of community stakeholders can unintentionally undermine TB control efforts by worsening the situation for people who already encounter numerous daily problems.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".