Need of global student safety and insurance day observance: a suggestion
Bibliographic record
Abstract
Child injuries are a growing global public health problem that requires urgent attention. They are a significant area of concern from the age of one year, and progressively contribute more to overall rates of death until the children reach adulthood. So, the authors suggested ‘student safety and insurance (SSI) day observance’ globally in order to create awareness against prevention of unintentional injuries (UII) and provision of SSI. The review focussed on estimation of the burden and causes of UIIs among students, determination of association of UIIs with socioeconomic factors, identification of the student safety day/week and SSI policies. A descriptive analysis of the articles published in various journals on UIIs among the students across the globe was undertaken. A systematic, predetermined strategy was undertaken for data collection, collation, compilation and assimilation. The authors found that the road traffic injuries alone are leading cause of death among 15-19 and the second leading cause among 10-14 years old (WHO-2008). In addition, millions of children require hospital care for non-fatal injuries. Many are left with some form of disability, often with lifelong consequences. Dr. Gururaj estimated that nearly 100,000 children died every year in India among 2,000,000 hospitalized. Certain universities/nations are observing student safety week. The authors concluded that children are particularly vulnerable group, either directly through being injured themselves or indirectly through the loss of parents. So, a convergent and cost benefit new initiative ‘global student safety and insurance day observance’ suggested every year in order to prevent all UIIs and to provide insurance.
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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 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.014 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.013 | 0.014 |
| Insufficient payload (model declined to judge) | 0.021 | 0.006 |
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".