KNOWLEDGE AND PRACTICES TOWARDS RABIES, ITS TREATMENT AND PREVENTION AMONG URBAN SLUM DWELLERS RESIDING IN GUWAHATI CITY – A CROSS SECTIONAL STUDY
Bibliographic record
Abstract
Rabies is one of the neglected tropical diseases (NTD) that predominantly affects already marginalized, poor and vulnerable populations. It is transmitted after the bite of a rabid animal, which are 99% cases of dog bite and is 100 % fatal if the timely intervention in terms of appropriate management of wound and Rabies post exposure prophylaxis is not given to the animal bite victims. Transmission can also occur if saliva of infected animals comes into direct contact [1] with mucosa (e.g. eyes or mouth) or fresh skin wounds. As per WHO estimates, globally, there are 59,000 human deaths due to dog-mediated rabies. India contributes to one third of the total global burden due to rabies and two-third of rabies burden in the South East Asia Region as per the WHO-APCRI [2] 2004 Survey. In 2015, the world called for action by setting a goal of “Zero human deaths due to dog-mediated rabies by [2] 2030”, worldwide. To achieve this target, the most important strategy should be to continuously and consistently raise mass awareness campaigns on health seeking behavior during the animal bites, proper animal bite wound management, and vaccination strategies among the general [3] public. Countries like Western Europe, Canada, the US, Mexico, Japan & Latin America have already eliminated dogmediated Rabies through successful canine rabies [2] vaccination and one health approach
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".