Socio-economic status and types of childhood injury in Alberta: a population based study
Bibliographic record
Abstract
BACKGROUND: Childhood injury is the leading cause of mortality, morbidity and permanent disability in children in the developed world. This research examines relationships between socio-economic status (SES), demographics, and types of childhood injury in the province of Alberta, Canada. METHODS: Secondary analysis was performed using administrative health care data provided by Alberta Health and Wellness on all children, aged 0 to 17 years, who had injuries treated by a physician, either in a physician's office, outpatient department, emergency room and/or as a hospital inpatient, between April 1st. 1995 to March 31st. 1996. Thirteen types of childhood injury were assessed with respect to age, gender and urban/rural location using ICD9 codes, and were related to SES as determined by an individual level SES indicator, the payment status of the Alberta provincial health insurance plan. The relationships between gender, SES, rural/urban status and injury type were determined using logistic regression. RESULTS: Twenty-four percent of Alberta children had an injury treated by physician during the one year period. Peak injury rates occurred about ages 2 and 13-17 years. All injury types except poisoning were more common in males. Injuries were more frequent in urban Alberta and in urban children with lower SES (receiving health care premium assistance). Among the four most common types of injury (78.6% of the total), superficial wounds and open wounds were more common among children with lower SES, while fractures and dislocations/sprains/strains were more common among children receiving no premium assistance. CONCLUSION: These results show that childhood injury in Alberta is a major health concern especially among males, children living in urban centres, and those living on welfare or have Treaty status. Most types of injury were more frequent in children of lower SES. Analysis of the three types of the healthcare premium subsidy allowed a more comprehensive picture of childhood injury with children whose families are on welfare and those of Treaty status presenting more frequently for an injury-related physician's consultation than other children. This report also demonstrates that administrative health care data can be usefully employed to describe injury patterns in children.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".