The Moderating Effects of Sex and Age on the Association between Traumatic Brain Injury and Harmful Psychological Correlates among Adolescents
Bibliographic record
Abstract
BACKGROUND: Although it is well established that sex is a risk factor in acquiring a traumatic brain injury (TBI) among adolescents, it has not been established whether it also moderates the influence of other TBI psychological health correlates. METHODS AND FINDINGS: Data were derived from a 2011 population-based cross-sectional school survey, which included 9,288 Ontario 7th-12th graders who completed anonymous self-administered questionnaires in classrooms. Response rate was 62%. Preliminary analyses found no evidence of nonresponse bias in the reporting of TBI. TBI was defined as a hit or blow to the head that resulted in a 5 minutes loss of consciousness or at least one overnight hospitalization due to symptoms associated with it. Reports of lifetime TBI were more common among males than females (23.1%, 95% CI: 20.5, 25.8 vs. 17.1%, 95% CI: 14.7, 19.8). Thirteen correlates were examined and included cigarette smoking, elevated psychological distress, suicide ideation, bully victimization (at school, as well as cyber bullying), bullying others, cannabis use, cannabis dependence and drug use problems, physical injuries, daily smoking, drinking alcohol, binge drinking, use of cannabis, and poor academic performance. Among the outcomes examined, sex moderated the relationship between lifetime TBI and cigarette smoking. In addition, sex and age jointly moderated the relationship between lifetime TBI and daily smoking, alcohol use and physical injuries. Late adolescent males who reported lifetime TBI, relative to females, displayed elevated daily smoking and injuries, whereas their females counterparts displayed elevated past year drinking. Possible bias related to self-report procedures and the preclusion of causal inferences due to the cross-sectional nature of the data are limitations of this study. CONCLUSIONS: TBI differences in outcomes need to be assessed for potential moderating effects of sex and age. Results have important implications for more tailored injury prevention efforts.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".