Strengthening accountability for TB policy implementation in South Africa: perspectives from policymakers, civil society and communities
Bibliographic record
Abstract
Abstract Background Translating health policy into effective implementation is a core priority for responding effectively to the TB crisis. The national TB Recovery Plan was developed in response to the negative impact that the COVID-19 pandemic had on TB care in South Africa. We aimed to explore the implementation of the TB Recovery Plan and develop recommendations for strengthening accountability for policy implementation for this and future TB policies. Methods We interviewed 24 participants working on or impacted by TB policy implementation in South Africa. This included perspectives from national, provincial, and local health department representatives, civil society, and community representatives. In-depth interviews were conducted in English and isiXhosa and we drew on reflexive thematic methods for analysis. Results Implementation of the TB Recovery Plan predominantly used a top-down approach to implementation (cascading from national policy to local implementers) but experienced bottlenecks at provincial level. Civil society organisations were concerned about the lack of provincial implementation data which impeded advocacy for better accountability. There was strong interest in community-led monitoring for local TB policy implementation from community representatives. Participants recommended broader multi-stakeholder engagement regarding TB as a public health priority and on new policies in the development and implementation phases. Conclusion Communities affected by TB, with the support of civil society organisations, could play a bigger role in monitoring policy implementation at local level and need to be capacitated to do this. This bottom-up approach could complement existing top-down strategies and contribute to greater accountability for TB policy implementation.
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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 | 0.066 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.024 | 0.023 |
| Scholarly communication | 0.021 | 0.019 |
| Open science | 0.002 | 0.018 |
| Research integrity | 0.008 | 0.015 |
| Insufficient payload (model declined to judge) | 0.005 | 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".