Determinants of unintentional injuries in preschool age children in high‐income countries: A systematic review
Bibliographic record
Abstract
BACKGROUND: Injuries are the leading cause of death and disability in preschool children who are subject to specific risk factors. We sought to clarify the determinants of unintentional injuries in children aged 5 years and under in high-income countries and report on the methodological quality of the selected studies. METHODS: A systematic review was conducted of observational studies investigating determinants of unintentional injury in children aged 0-5. Searches were conducted in Web of Science, Medline, Embase, PsycInfo and CINAHL. All methods of data analysis and reporting followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA 2021) guidelines. Determinants are reported at the child, parental, household and area level. RESULTS: An initial search revealed 6179 records. Nineteen studies met the inclusion criteria: 17 cohort studies and 2 case control studies. While studies included longitudinal surveys and administrative healthcare data analysis, the highest quality studies examined were case-control designs. Child factors associated with unintentional injury include male gender, age of the child at the time of injury, advanced gross motor score, sleeping problems, birth order, attention deficit hyperactivity disorder (ADHD) diagnosis and below average score on the standard strengths and difficulties scale. Parental factors associated with unintentional injuries included younger parenthood, poor maternal mental health, hazardous or harmful drinking by an adult within the home, substance misuse, low maternal education, low paternal involvement in childcare and routine and manual socioeconomic classification. Household factors associated with injury were social rented accommodation, single-parent household, White ethnicity in the United Kingdom, number of children in the home and parental perception of a disorganised home environment. Area-level factors associated with injury were area-level deprivation and geographic remoteness. CONCLUSION: Child factors were the strongest risk factors for injury, whereas parental factors were the most consistent. Further research is needed to examine the role of supervision in the relationships between these risk factors and injury. Injury intent should be considered in studies using administrative healthcare data. Prospective research may consider utilising linked survey and administrative data to counter the inherent weaknesses of these research approaches.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".