Quality of care for tuberculosis and HIV in the private health sector: a cross-sectional, standardised patient study in South Africa
Bibliographic record
Abstract
BACKGROUND: South Africa has high burdens of tuberculosis (TB) and TB-HIV, yet the quality of patient care in the private sector is unknown. We describe quality of TB and TB-HIV care among private general practitioners (GPs) in two South African cities using standardised patients (SPs). METHODS: Sixteen SPs presented one of three cases during unannounced visits to private GPs in selected high-TB burden communities in Durban and Cape Town: case 1, typical TB symptoms, HIV-positive; case 2, TB-specified laboratory report, HIV-negative and case 3, history of incomplete TB treatment, HIV-positive. Clinical practices were recorded in standardised exit interviews. Ideal management was defined as relevant testing or public sector referral for any reason. The difference between knowledge and practice (know-do gap) was assessed through case 1 vignettes among 25% of GPs. Factors associated with ideal management were assessed using bivariate logistic regression. RESULTS: 511 SP visits were completed with 212 GPs. Respectively, TB and HIV were ideally managed in 43% (95% CI 36% to 50%) and 41% (95% CI 34% to 48%) of case 1, 85% (95% CI 78% to 90%) and 61% (95% CI 73% to 86%) of case 2 and 69% (95% CI 61% to 76%) and 80% (95% CI 52% to 68%) of case 3 presentations. HIV status was queried in 35% (95% CI 31% to 39%) of visits, least with case 1 (24%, 95% CI 18% to 30%). The difference between knowledge and practice was 80% versus 43% for TB and 55% versus 37% for HIV, resulting in know-do gaps of 37% (95% CI 19% to 55%) and 18% (95% CI -1% to 38%), respectively. Ideal TB management was associated with longer visit time (OR=1.1, 95% CI 1.1 to 1.2), female GPs (3.2, 95% CI 2.0 to 5.1), basic symptom inquiry (2.0, 95% CI 1.7 to 2.3), HIV-status inquiry (OR=11.2, 95% CI 6.4 to 19.6), fewer medications dispensed (OR=0.6, 95% CI 0.5 to 0.7) and Cape Town (OR=2.2, 95% CI 1.5 to 3.1). Similar associations were observed for HIV. CONCLUSIONS: Private providers ideally managed TB more often when a diagnosis or history of TB was implied or provided. Management of HIV in the context of TB was less than optimal.
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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 | 0.001 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".