On Redefining Telemedicine Paradigm: An Innovative Integrated Model for Efficient Implementation of Healthcare Delivery in Developing Countries
Bibliographic record
Abstract
On Redefining Telemedicine Paradigm: An Innovative Integrated Model for Efficient Implementation .... 141 2.1 The facts on India's health care situation are as follows 620 million live in rural India (National Council for Applied Economic Research (NCAER)) Bed-Population ratio 1.85 per thousand (2005) Vs. ideal of 1:500 Central bereau of health intelligence CBHI) Doctor-patient ratio in the country is one doctor for nearly 2,000 persons (in the US it is 1:400), 2 million beds are required as against 0.7 million available. 700 hospitals of 250 beds each are required every year. only 9% of 1 billion people are covered health schemes. only 0.9% of GDP for health (WHO recommends 5%) 5% of annual family income spent towards curative health care.The distribution of specialists in India is indeed lopsided.There are more neurologists and neurosurgeons in Chennai, than in all the states of North eastern India put together.Similarly tertiary care hospitals are also concentrated in pockets with large segments of the population having no access.India is short of 600,000 doctors, 1 million nurses, and 200,000 dental surgeons to achieve 1:10,000 doctor-patient ratio.A recent survey by the Indian Medical society has found 75% of qualified consulting doctors practice in urban centers and 23% in semi urban areas and only 2% from rural areas whereas majority of the patients come from rural areas. .Contagious, infectious and waterborne diseases such as diarrhoea, amoebiasis, typhoid, infectious hepatitis, worm infestations, measles, malaria, tuberculosis, whooping cough, respiratory infections, pneumonia and reproductive tract infections dominate the morbidity pattern, especially in rural areas.However, non-communicable diseases such as cancer, blindness, mental illness, hypertension, diabetes, HIV / AIDS, etc. are also on the rise.Health being a State of subject in every human life, the data in regard to Doctor-patient ratio (D:P)in various State Government Hospitals is not maintained centrally.The doctorpatient ratio, varies from case to case depending upon various factors like the type of disease, nature of specialization, type of patient-care required i.e. indoor/outdoor.According to the Medical Council of india, the present allopathic doctor-population ratio at present works out to 1:1722.The health of a nation is the product of many factors and forces that combine and interact.Economic growth, per capita income, literacy, education, age at marriage, birth rates, information on health care and nutrition, access to safe drinking water, public and private health care infrastructure, access to preventive health and medical care and the health insurance are among the contributing factors.Given that many conditions are preventable, every health care interaction should include prevention support.When patients are systematically provided with information and skills to reduce health risks, substance use, stop using tobacco products, practice safe sex, eat healthy foods, and engage in physical activity can dramatically reduce the long-term burden and health care demands of chronic conditions.To promote prevention in health care: awareness rising, change in thinking, stimulate the commitment of patients and families, health care teams, communities and policy-makers is crucial.A collaborative management approach at the primary health care level with patients, their families and other health care actors is a must to effectively prevent many major contributors to the burden of disease.Given that many conditions are preventable, every health care interaction should be recorded. How to referenceIn order to correctly reference this scholarly work, feel free to copy and paste the following: K.V.
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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 | 0.010 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".