What Constitutes Health Care Seeking Pathway of TB Patients: A Qualitative Study in Rural Bangladesh
Bibliographic record
Abstract
Given the targeted 4-5% annual reduction of tuberculosis (TB) cure cases to reach the "End TB Strategy" by 2020 milestone globally set by WHO, exploration of TB health seeking behavior is warranted for insightful understanding. This qualitative study aims to provide an account of the social, cultural, and socioeconomic breadth of TB cases in Bangladesh. We carried out a total of 32 In-depth Interviews (IDIs) and 16 Key Informant Interviews (KIIs) in both rural and urban areas of Bangladesh. We covered both BRAC [a multinational Non-governmental Organization (NGO)] and non-BRAC (other NGOs) TB program coverage areas to get an insight. We used purposive sampling strategy and initially followed "snowball sampling technique" to identify TB patients. Neuman's three-phase coding system was adopted to analyze the qualitative data. Underestimation of TB knowledge and lack of awareness among the TB patients along with the opinions from their family members played key roles on their TB health seeking behavior. Quick decision on the treatment issue was observed once the diagnosis was confirmed; however, difficulties were in accepting the diseases. Nevertheless, individual beliefs, intrinsic ideologies, financial abilities, and cultural and social beliefs on TB were closely inter-connected with the "social perception" of TB that eventually influenced the care seeking pathways of TB patients in various ways. Individual and community level public health interventions could increase early diagnosis; therefore, reduce recurrent TB.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".