Provider perspectives on empirical antibiotic treatment for tuberculosis-like symptoms in South Africa’s private general practice sector: A qualitative study in two cities
Bibliographic record
Abstract
While tuberculosis (TB) in South Africa is commonly treated in the public sector, some clients first seek care in the private sector. Research has demonstrated that private general practitioners (GPs) perform less well than do public sector care providers in TB testing and drug-dispensing practices. We aimed to describe GPs' decision-making practices related to empiric antibiotic treatment when presented with symptoms that may be related to TB, to inform potential interventions. Within a larger study on private sector TB management, we qualitatively interviewed 30 purposively selected GPs, who varied by gender, age, practice community, and how they managed TB and HIV in the parent study. Data were analysed through coding and constant comparison. GPs acknowledged the common use of broad-spectrum antibiotics for respiratory symptoms, driven by experience treating presumed bacterial infections and by a desire to rule out other causes before referring clients for potentially inconvenient TB tests in the private or public sector. Management decisions were susceptible to perceived or expressed pressure from clients, who may expect on-the-spot treatment. Additionally, GPs indicated using antibiotics to mitigate financial strain on economically vulnerable clients. Empirical antibiotic treatment for presentations that may be related to TB in the private sector, which can delay TB diagnosis, could be explained by the absence of accessible and affordable TB and general bacteriologic tests at the point of care, leading GPs to, among others, seek to 'rule out' possible bacterial infection. Potential interventions include increasing the salience of inappropriate antibiotic use, heightening GPs' suspicion index for TB, and linking GPs directly to public sector TB tests for clients.
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.010 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".