Examining the Relationship Between Traumatic Brain Injury and Substance Use Outcomes in the Canadian Population
Bibliographic record
Abstract
BACKGROUND: The literature has opposing views regarding the magnitude of the association between substance use and TBI. Most studies have examined clinical samples which are not representative of the entire head injured population. Clinical samples provide very limited insight into TBI patients whom do not seek care. OBJECTIVES: This paper examines the associations between TBI and substance use/misuse. Its primary aim is to test whether or not individuals with a past-year TBI have higher rates of substance use/misuse than Canadians without a TBI or back and/or spine injury controls drawing on self-report population level data. METHODS: Using the 2009-2010 Canadian Community Health Survey, a nationally representative cross-sectional survey of Canadians 12 years and older, this paper assessed substance use (i.e., illicit drug use; drinking and binge drinking; current smoking) among those with a TBI, as compared to two control groups: (1) individuals with a back or spinal injury (BSI); and (2) healthy noninjured controls. Multivariate regressions (logistic and multinomial), both unadjusted and adjusting for a range of injury and sociodemographic covariates, were used in hypothesis testing. RESULTS: Those with a past-year TBI demonstrated significantly elevated rates of illicit drug use relative to non-injured Canadians. Relative to the BSI group those with a TBI were less likely to drink alcohol, did not differ in binge drinking, cigarette smoking and illicit drug use. CONCLUSION: Health care professionals working with the TBI population should integrate screening, brief intervention, and referral programming as a means to reduce future harm related to substance misuse.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 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) | 0.002 | 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".