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 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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| 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 | 0.001 |
| 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".