For Canadian infants, 1 hospitalization in 20 is injury related, and the rate increases to
Bibliographic record
Abstract
year olds. For 10-14 year olds, injuries are the leading cause of hospitalization; almost 1 in 4 hospitalizations are injury related. According to Canada’s 1993 General Social Survey, 11 % of children aged 0-15 years sustained an injury severe enough to require a visit to the doctor. In contrast to our knowledge of injury and hospital use in children, we know relatively little about vis-its to other medical practitioners. Much of our current knowledge of the medical services used to treat injuries at the population level comes from hospital data (e.g., provincial hospital databases, Canadian Institute for Health Information; Ontario Health Survey). We know less about patterns of health service utilization associated with those children who suffer injuries who are cared for in the home and are not seen at the hospital. The impact of injury includes costs and services beyond hospital fees, particularly for those recovering from injury and requiring on-going treatment from health care providers. Furthermore, children who have a history of injury experiences are more likely to encounter frequent injuries as well as more numerous future injuries requiring medical care.1 This study examines associations between maternal reports of childhood injuries during the last 12 months and vis-its to medical practitioners by age group and gender of the child. In our models we include factors that have been shown to influence both injuries and health service use including gender of the child as well as family socio-demographic indicators such as marital status, household income, house-hold size, and maternal levels of education.2
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.055 | 0.006 |
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".