Exploring Health Inequities: Head Injuries in People Experiencing Homelessness
Bibliographic record
Abstract
Lifetime occurrence of head injury is disproportionately affecting people experiencing homelessness in Canada. Head injury in people experiencing homelessness is associated with victimization, housing instability and substance use (Topolovec-Vranic et al., 2017). However, individual factors including sex, race, social class and disability also produce social and health inequities which may have intersectional impacts on this population (McCall, 2005). Through secondary exploration of data from the No Fixed Address Version 2 (NFAv2) and No Fixed Address Version 2x (NFAv2x) studies (Forchuk et al., 2018; in press), and an intersectional lens, the purpose of this study was to explore relationships between individual factors as well as risks in relation to head injury in people experiencing homelessness. Four of the independent variables were statistically significant in the binary logistic regression model including; education, mental health issues, physical health issues and victimization. Findings help to explain multiple inequities faced by people experiencing homelessness that shape their experiences with head injury. Further research is needed to develop a greater understanding of head injury in people experiencing homelessness.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".