Stroke care challenges in rural India: awareness of causes, preventive measures and treatment options of stroke among the rural communities.
Bibliographic record
Abstract
Introduction: Management of stroke in the remote rural areas in India faces major challenges because of lack of awareness. Stroke care services can be optimally implemented only if the communities have an understanding of the disease. Method: A population based, cross sectional survey of an adult general population sample between the ages of 31-60 years in a rural block in Tamil Nadu, India was carried out to study their knowledge, attitude, beliefs about cause, signs and symptoms, preventive measures and treatment options of stroke. Results: Of the 174 subjects studied only 69% were aware of the term stroke and 63% were able to list the symptoms. Only a little more than half the participants (58%) were aware that diabetes, smoking and hypertension are risk factors for stroke. None of the participants were aware of the endovascular thrombolysis injection for better recovery from stroke. About quarter (23%) of the participants did not think that the stroke is an emergency condition and they need to take the patient urgently to the hospital. Only 56% of the participants had checked their blood pressure and 49% for diabetes. A history of having either hypertension or diabetes and stroke in the family was the only factor that was significantly associated with better awareness (p=<0.001) independent of other potential facilitating factors including age, occupation, education and gender. Conclusion: There is a need to educate the rural communities about the risk factors, how to recognize the onset, the preventive measures and optimum care of stroke to reduce the burden.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".