Is your hospital safe for children? Applying home safety principles to the hospital setting
Bibliographic record
Abstract
OBJECTIVES: To review the risks of injury to children in the hospital setting and to provide an overview of the factors which influence the approach to hospital safety, including institutional liability, hospital accreditation, patient safety and risk management issues. METHODS: Fatal and nonfatal injuries to children in the hospital setting were identified using searches of the published literature and searches of incident, complaint and claims data sources, including regulatory agency databases, litigation and claims data, and medical device hazard databases. Canadian hospital law, accreditation, patient safety and risk management literature was reviewed and summarized. RESULTS: Injuries occur in over 1% of hospitalized children, and are typically due to falls. Serious injuries are infrequent; however, a significant number of fatal injuries have been reported, mostly involving entrapment in beds and cribs, but also due to choking, strangulation and electrocution. Hospitals are liable for injuries to patients and visitors occurring on their premises. Canadian accreditation standards include provisions for the safety of equipment, supplies, medical devices and space, but do not provide specific guidance for children. Addressing injury hazards to children is an important aspect of the new patient safety movement, and falls within the scope of existing risk management and quality improvement programs. CONCLUSIONS: Most hazards to children in the hospital setting can be easily corrected by proactively incorporating basic child safety principles. Paediatricians can play an important role in advocating for a safe hospital environment and should encourage administrators to consider child safety in routine hospital operation and policies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".