Childhood unintentional injury: The impact of family income, education level, occupation status, and other measures of socioeconomic status. A systematic review
Bibliographic record
Abstract
INTRODUCTION: Unintentional injuries represent a substantial public health burden among children and adolescents, and previous evidence suggests that there are disparities in injury by socioeconomic status (SES). This paper reports on a systematic review of literature on injury rates among children and adolescents by measures of SES. METHODS: A systematic literature search was conducted using six electronic databases: MEDLINE, PsycINFO, CINAHL, HealthSTAR, EMBASE, and SportsDiscus. This review considered children ages 19 years and under and publications between 1997 and 2017-representing an update since the last systematic review examined this specific question. Fifty-four articles were summarized based on study and participant descriptions, outcome and exposure, statistical tests used, effect estimates, and overall significance. RESULTS: Most articles addressed risk factors across all injury mechanisms; however, some focused particularly on burns/scalds, road traffic injuries, falls/drowning cases, and playground/sports injuries. Other studies reported on specific injury types including traumatic dental injuries, traumatic brain injuries, and fractures. The studies were of moderate quality, with a median of 15.5 (95% confidence interval [CI]: 15.34 to 15.66) out of 19. Thirty-two studies found an inverse association between SES and childhood unintentional injury, three found a positive association while twenty were not significant or failed to report effect measures. CONCLUSION: Given the variability in definition of the exposure (SES) and outcome (injury), the results of this review were mixed; however, the majority of studies supported a relationship between low SES and increased injury risk. Public health practice must consider SES, and other measures of health equity, in childhood injury prevention programming, and policy.
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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 | 0.008 | 0.036 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.011 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".