Bibliographic record
Abstract
Concussion has become a significant public health concern among Canadian youth, as estimates of pediatric concussion incidence have increased from 340.5 per 100,000 in 2003 to 601.3 in 2010, and 1500 in 2013. This recent surge in concussion diagnosis has led to extensive research into the physiological mechanisms underlying traumatic brain injury, as well as sport-focused policies and return to play protocols following concussion. However, there is a paucity of research regarding social and behavioural risk factors for the development of a concussion. Multiple risk behaviours (MRB) represent a clustering of behaviours that often develop together during adolescence, such as alcohol consumption, illicit drug use and unprotected sex. These behaviours indicate an increased tendency for risk-taking, and have previously been associated with an increased risk for injury. Although the current literature describes a consistent injury risk gradient associated with increasing engagement in MRB, few studies have examined the relationship between engagement in MRB and the incidence of specific injuries. This study further investigates pediatric concussion through two research objectives. The first objective is to describe concussion prevalence, differences in prevalence by age and sex, as well as activity leading to concussion, among Canadian youth in grades 6-10. The second objective is to investigate the relationship between engagement in multiple risk behaviours and concussion within the same population. It is hypothesized that children engaging in greater risky behaviour will exhibit higher concussion prevalence. The results of this study may be used to inform behavioural interventions designed to reduce concussion in youth.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".