Inadequate healthcare facilities despite Endosulfan affected area in Kasaragod District
Bibliographic record
Abstract
At Kasaragod district in Kerala state the inhabitants here suffer due to the negligence of states own Plantation Corporation by 20 years spraying of Endosulfan pesticide in 2000 hectares of cashew plantation. It has no choice but to compensate for the unparalleled misery caused by the silent killer Endosulfan for a quarter of a century. More than thousands of people have been suffering by this environmental manmade disaster but the ratio between doctor and population rate is lower than the recommendation of World health organization. Unemployment in the district pushes international and national migration of the Kasaragod citizens. The rate of migration and birth are high in Kasaragod district. Lack of Tertiary hospitals in Kasaragod the Inhabitants in the district not only depends by near city for multi-specialty healthcare but also for better education, employment and other facilities. This dependence is the main reason for inadequate healthcare system and under development of the district. The statistical methods are used to measure healthcare facilities and population in Kasaragod district. Accessibility towards nearby cities have analyzed by Geographical Information System software. Here Patients were struggled for consultation in the emergency of Covid pandemic period. Basic facility of a society can measures by considering its quality of health care centers. But in Kasaragod patients are rely for multi-specialty hospital to neighborhood cities. If they have to take effort for treatment by long journey pointing out that the district has insufficient health care system in it.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".