Perceptions, practices and health seeking behaviour constrain JE/AES interventions in high endemic district of North India
Bibliographic record
Abstract
BACKGROUND: Acute Encephalitis Syndrome (AES) and Japanese Encephalitis (JE) stay as poorly understood phenomena in India. Multiple linkages to determinants such as poverty, socio-economic status, gender, environment, and population distribution, make it a greater developmental issue than just a zoonotic disease. METHODS: A qualitative study was conducted to map knowledge, perceptions and practices of community and health systems level stakeholders. Seventeen interviews with utilizers of AES care, care givers from human and veterinary sectors, Non-governmental Organizations (NGOs), and pig owners and 4 Focused Group Discussions (FGDs) with farmers, community leaders, and students were conducted in an endemic north Indian district-Kushinagar. RESULTS: Core themes that emerged were: JE/AES been perceived as a deadly disease, but not a major health problem; filthy conditions, filthy water and mosquitoes seen to be associated with JE/AES; pigs not seen as a source of infection; minimal role of government health workers in the first-contact care of acute Illness; no social or cultural resistance to JE vaccination or mosquito control; no gender-based discrimination in the care of acute Illness; and non-utilization of funds available with local self govt. Serious challenges and systematic failures in delivery of care during acute illness, which can critically inform the health systems, were also identified. CONCLUSION: There is an urgent need for promotive interventions to address lack of awareness about the drivers of JE/AES. Delivery of care during acute illness suffers with formidable challenges and systematic failures. A large portion of mortality can be prevented by early institution of rational management at primary and secondary level, and by avoiding wastage of time and resources for investigations and medications that are not actually required.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".