21 Victoria healthy youth survey injury analysis
Bibliographic record
Abstract
Statement of purpose Injuries in the adolescents are higher than in any other age group and are amongst the leading causes of disability and death. This study aims to identify factors contributing to injury in a population transitioning into adulthood. Methods/approach Data for this report was drawn from the Victoria Health Youth Survey (VHYS), a random sample, longitudinal study that ran biannually from 2003–2011. Youth aged 14–21 self-reported information on the type, location, cause and other factors associated with the injury. Univariate analysis consisted of t-tests for continuous and X2 for categorical data. Results In total, 662 individuals participated in 2003, out of which 463 (70%) were available for analysis in 2011. Overall, males were consistently more likely to get injured. As participants got older, they got injured less, were more likely to consume alcohol at the time of injury and tended to take more preventable measures after an injury. Most common types of injuries were sprains/strains and broken bones/bruises. Individuals that exercised frequently were also about 10% more likely to get injured. About 6% of all occurred injuries were concussions. At baseline, most injuries occurred in schools (27%), outside in a park or recreation area (27%) or inside a sports arena/recreation centre (15%). The vast majority of all injuries were unintentional, or non-aggressive in nature (>94%). Conclusions Drug use, emotional impact, SES, education, average hours of sleep and self-rated physical and mental health did not vary significantly between injured and non-injured participants across the study years. Enhanced knowledge of factors that could influence injury occurrence can improve injury prevention strategies and enhance injury epidemiology research. Significance and contributions This is one of the few studies to look at injuries in a transitioning, youth population. All of the authors contributed significantly to the design, conception and interpretation of the 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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".