Student safety insurance policies in India: a systematic review
Bibliographic record
Abstract
Personal accident insurance policy provides complete financial protection to the insured members against uncertainties such as accidental (unintentional) death or bodily injuries. Additionally, it covers permanent (partial/complete) and temporary disabilities resulting from an unintentional injury (UII), but not intentional injury. As such policy confined to the students, it was nomenclated as ‘student safety insurance policy’ (SSIP). Aim of the study was to review the features of various SSIPs in India followed by suggesting to the governments to implement across India. A thorough internet search was carried out using search engines to collect primary data in the public domain regarding SSIPs. Additional information was also collected from the organizations under Right to Information Act 2005 during July to December 2017. The reviewers identified that millions of children died each year from the injuries or violence and millions of others suffer the consequences of non-fatal injuries across the world. Dr. Gururaj estimated that the injuries resulted in the deaths of nearly 100,000 children every year in India among two million children hospitalized. So, certain state governments, districts and universities have taken up ‘SSIPs’ with different features to meet the hospital expenses incurred due to an UII. Unfortunately, if the insured child died/disabled due to an UII, the insurance amount will fulfil future financial needs of the family/disabled. Certain institutions are providing insurance coverage for one of the parents (breadwinner) and exempting future course fee payment also. The reviewers suggested to the government to provide a better student safety insurance policy across India with the amalgamation of the key features of all safety insurance policies.
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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 | 0.013 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| 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".