Exploring current and potential roles of informal healthcare providers in tuberculosis care in West Bengal, India: a qualitative content analysis
Bibliographic record
Abstract
Abstract India accounts for 27 percent of global Tuberculosis (TB) cases, the highest among the 30 high-burden countries. Despite growing evidence highlighting the significance and potential of Informal Healthcare Providers (IPs) in TB care, their role remains ambiguous in India’s TB policies and guidelines, in contrast to the well-defined roles of the formal private sector. Considering such gaps, this study explores the perspectives of IPs and Formal Providers (FPs) regarding IPs’ current and potential roles in TB care. The study was conducted in West Bengal, India. We adopted a qualitative approach and conducted in-depth interviews with 23 IPs and 11 FPs. The study data was analysed using a content analysis approach. The study’s findings identified four current roles of IPs in TB care, two of which were corroborated by FPs: 1) Passive case finding and referral and 2) Treatment supporter. As for potential roles, an alignment was observed between the two groups of providers for the majority of the roles (5/7 roles). However, both IPs and FPs expressed reservations about assigning IPs the roles of 1) Clinical evaluation of TB cases and 2) Initiation of treatment for confirmed TB patients. The findings highlight the active involvement of IPs in various TB care roles, acknowledged by FPs, and also demonstrate significant potential for their expanded engagement under the National TB Elimination Program (NTEP) of India.
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".