Child Injury: The Role of Supervision in Prevention
Bibliographic record
Abstract
As the leading cause of death and major contributor to hospitalization for children, unintentional injury is a significant health problem in the United States. How supervision influences children’s risk of injury has been of interest for some time, and much progress has been made recently to address definitional and measurement issues pertaining to supervision. Increasing evidence supports the notion of a general relationship between increased supervision and decreased injury risk, but also reveals that child behavioral attributes and environmental characteristics can interact with level of supervision to affect injury risk, making it challenging to develop guidelines regarding what constitutes “adequate” supervision. Further research is needed to explore if and how children’s risk of injury varies with different supervisors (eg, mothers vs fathers vs older siblings) and how these relations change as a function of children’s developmental level. Recent research has identified messaging approaches that are effective to invoke a commitment to more closely supervising young children at home. Examining how these messages affect actual supervisory practices is an essential next step in this research and can support the development of evidence-based programs to improve supervision and reduce children’s risk of injuries.
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 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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".