Empowering Reality: A New Injury Prevention Education System to Promote the Empowerment of Child Caregivers
Bibliographic record
Abstract
Although awareness about the importance of injury prevention has been increasing among Japanese people, preventable injuries remain the third leading cause of death in children aged 0–14 years, and prevention of these injuries is critically important in terms of childhood health. To identify dangerous situations for children and provide preventive measures to avoid such situations, this paper proposes an effective method, called “Empowering Reality (ER)”, that integrates knowledge graphs with object detection to enable lecturers to educate caregivers on preventing unintentional childhood injuries while communicating with caregivers using augmented reality technology. The proposed ER system consists of knowledge graphs for explaining dangerous situations, an online video capture part, and a situation recognition part. This paper describes the major advantages of knowledge graphs that consider not only the relationship between objects and injuries, but also dangerous layouts with the help of “inclusion” and “collocation” features. The feasibility and effectiveness of the system were evaluated through tests among caregivers, including 11 parents and six teachers from three nursery schools. This system allows lecturers to conduct in-situ suggestions about specific preventive measures adapted to the home or nursery school environment via online learning
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".