Alternative medicine: an ethnographic study of how practitioners of Indian medical systems manage TB in Mumbai
Bibliographic record
Abstract
BACKGROUND: Mumbai is a hot spot for drug-resistant TB, and private practitioners trained in AYUSH systems (Ayurveda, yoga, Unani, Siddha and homeopathy) are major healthcare providers. It is important to understand how AYUSH practitioners manage patients with TB or presumptive TB. METHODS: We conducted semi-structured interviews of 175 Mumbai slum-based practitioners holding degrees in Ayurveda, homeopathy and Unani. Most providers gave multiple interviews. We observed 10 providers in clinical interactions, documenting: clinical examinations, symptoms, history taking, prescriptions and diagnostic tests. RESULTS: No practitioners exclusively used his or her system of training. The practice of biomedicine is frequent, with practitioners often using biomedical disease categories and diagnostics. The use of homeopathy was rare (only 4% of consultations with homeopaths resulted in homeopathic remedies) and Ayurveda rarer (3% of consultations). For TB, all mentioned chest x-ray while 31 (17.7%) mentioned sputum smear as a TB test. One hundred and sixty-four practitioners (93.7%) reported referring TB patients to a public hospital or chest physician. Eleven practitioners (6.3%) reported treating patients with TB. Nine (5.1%) reported treating patients with drug-susceptible TB with at least one second-line drug. CONCLUSIONS: Important sources of health care in Mumbai's slums, AYUSH physicians frequently use biomedical therapies and most refer patients with TB to chest physicians or the public sector. They are integral to TB care and control.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".