Healthcare Utilization, Legal Incidents, and Victimization Following Traumatic Brain Injury in Homeless and Vulnerably Housed Individuals
Bibliographic record
Abstract
OBJECTIVE: To characterize the associations between a history of traumatic brain injury (TBI) and subsequent healthcare utilization, legal involvement, and victimization. SETTING: Three major Canadian cities. PARTICIPANTS: A total of 1181 homeless and vulnerably housed adults who were single and 18 years or older. Data for 968 participants (82%) were available at 1-year follow-up. DESIGN: Prospective cohort study. Data were collected using structured, in-person interviews at baseline in 2009 and approximately 1 year after baseline. MAIN MEASURES: Self-reported TBI, 12-item Short Form Health Survey, healthcare, and criminal justice use questionnaires. RESULTS: The lifetime prevalence of TBI was 61%. A history of TBI was independently associated with emergency department (ED) use [adjusted odds ratio (AOR) = 1.5, 95% confidence interval (CI): 1.11-1.96], being arrested or incarcerated (AOR = 1.79, 95% CI: 1.3-2.48) and being a victim of physical assault (AOR = 2.81, 95% CI: 1.96-4.03) during the 1-year follow-up period. CONCLUSIONS: Homeless and vulnerably housed individuals with a lifetime history of TBI are more likely to be ED users, arrested or incarcerated, and victims of physical assault over a 1-year follow-up period even after adjustment for health status and other confounders. These findings have public health and criminal justice implications and highlight the need for effective screening, treatment, and rehabilitation for TBI in this population.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".