Capturing paediatric injury in Ontario: differences in injury incidence using self-reported survey and health service utilisation data
Bibliographic record
Abstract
OBJECTIVE: Population-based health surveys are increasingly popular sources of data on injury occurrence. Self-reported surveys can yield estimates of the total incidence of non-fatal injuries while simultaneously capturing a rich repository of contextual data that may be informative for exploring determinants of injury risk. Although survey data are rarely recognised as complete, several researchers have expressed concerns about the sensitivity and validity of self-reported injury data, questioning whether captured cases are representative of the population experience of injury, particularly among children and youth. The present study sought to compare the population incidence of paediatric injury estimated from self-reported survey responses to those documented by a complete-capture health service utilisation database among Ontario children. METHODS: Injury incidence rates documented from the National Longitudinal Survey of Children and Youth and the National Population Health Survey were compared with those reported in Canada's National Ambulatory Care Reporting System for Ontario youth aged 0-19 years for fiscal year 2002/3, stratified by the child's age and geographical location of residence. RESULTS: The two self-reported health surveys underestimated the population incidence of injury among Ontario children by at least 49% and 53%, respectively. Systematic errors exist in survey data capture such that injuries in infants and preschoolers (<4 years of age) and urban residents were most likely to be missed by the population health surveys. CONCLUSION: Injury incidence estimated through self-report is not representative of the population burden and experience of paediatric injury for Ontario children, and may produce biased estimates of risk when analysed as independent sources of data.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".