Barriers and facilitators to informal healthcare provider engagement in the national tuberculosis elimination program of India: An exploratory study from West Bengal
Bibliographic record
Abstract
India has a high burden of Tuberculosis (TB), accounting for a significant portion of global cases. While efforts are being made to engage the formal private sector in the National TB Elimination Program (NTEP) of India, there remains a significant gap in addressing the engagement of Informal Healthcare Providers (IPs), who serve as the first point of contact for healthcare in many communities. Recognizing the increasing evidence of IPs' importance in TB care, it is crucial to enhance their engagement in the NTEP. Therefore, this study explored various factors influencing the engagement of IPs in the program. A qualitative study was conducted in West Bengal, India, involving 23 IPs and 11 Formal Providers (FPs) from different levels of the formal health system. Thematic analysis of the data was conducted following a six-step approach outlined by Braun and Clarke. Three overarching themes were identified in the analysis, encompassing barriers and facilitators to IPs' engagement in the NTEP. The first theme focused on IPs' position and capacity as care providers, highlighting their role as primary care providers and the trust and acceptance extended by the community. The second theme explored policy and system-level drivers and prohibitors, revealing barriers such as role ambiguity, competing tasks, and quality of care issues. Facilitators such as growing recognition of IPs' importance in the health system, an inclusive incentive system, and willingness to collaborate were also identified. The third theme focused on the relationship between the formal and informal systems, highlighting a need to strengthen the relationship between the two. This study sheds light on factors influencing the engagement of IPs in the NTEP of India. It emphasizes the need for role clarity, knowledge enhancement, and improved relationships between formal and informal systems. By addressing these factors, policymakers and stakeholders can strengthen the engagement of IPs in the NTEP.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".