Gaps in Childhood Injury Research and Prevention: What Can Developmental Scientists Contribute?
Bibliographic record
Abstract
Abstract Unintentional injury is the leading cause of pediatric mortality in most of the developed world. Contributions from epidemiology, pubic health, and engineering perspectives have yielded important insights into risk and protective factors, but recent calls for research stress the need for behavioral science to advance understanding and prevention of childhood injuries. Limiting its focus to children younger than 13 years, this article identifies 4 gaps in the literature on childhood injury and discusses how developmental science might address these research needs by (a) applying developmental theory and conceptual approaches to understand the processes by which children are injured, (b) examining the role of developmental processes in injury risk, (c) identifying the bases for group differences in injury related to gender and cultural influences, and (d) exploring how family processes and relationships affect injury risk.
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 | 0.076 | 0.161 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.002 |
| Bibliometrics | 0.012 | 0.008 |
| Science and technology studies | 0.005 | 0.013 |
| Scholarly communication | 0.014 | 0.023 |
| Open science | 0.006 | 0.014 |
| Research integrity | 0.014 | 0.016 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".