Quality of Sexually Transmitted Disease Treatments in the Formal and Informal Sectors of Bangui, Central African Republic
Bibliographic record
Abstract
BACKGROUND: Interventions for upgrading sexually transmitted disease (STD) management in sub-Saharan Africa have focused on the public sector, and to a much lower extent on private medical practitioners and pharmacies. However, in most African cities there is a large informal sector that provides care to many patients with STD symptoms. GOAL: To compare the quality of treatments offered to patients with major STD syndromes in the public sector, pharmacies, and the informal sector of the same city. STUDY DESIGN: Healthcare providers in health centers, pharmacies, private laboratories, and market drug peddlers in Bangui, Central African Republic, were asked to complete a short form for every patient consulting them with genital complaints. The treatments they ordered were evaluated for their potential efficacy against the major etiologic agents of the syndrome for which the patient consulted. RESULTS: The majority of male patients with STDs preferred to seek care in pharmacies and in the informal sector. The STD treatments offered to patients with urethral discharge or genital ulcers in pharmacies and in the informal sector tended to focus on a single etiologic agent. The quality of STD treatments offered by drug peddlers and private laboratories was poor, apart from adequate coverage of syphilis in patients with genital ulcers and of candidiasis in women with vaginal discharge. For instance, 41% and 34% of patients with urethral discharge managed by drug peddlers and private laboratories did not receive a drug active against either Neisseria gonorrhoeae or Chlamydia trachomatis, whereas this proportion was 22% in pharmacies and 14% in health centers. For patients with genital ulcers, the proportion offered a drug active against Haemophilus ducreyi was 2% if seen by drug peddlers, 0% in laboratories, 10% in pharmacies, and 25% in health centers. For each syndrome and each category of provider, between one fourth and two thirds of patients had already received another ineffective treatment elsewhere. CONCLUSION: National STD and HIV control programs will have to improve STD management in pharmacies and in the informal sector if they are to have any impact on the dynamics of HIV infection in urban centers.
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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 | 0.003 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".