Hospitalizations due to unintentional transport injuries among Aboriginal population of British Columbia, Canada: Incidence, changes over time and ecological analysis of risk markers
Bibliographic record
Abstract
BACKGROUND: Worldwide, Indigenous people have disproportionately higher rates of transport injuries. We examined disparities in injury-related hospitalizations resulting from transport incidents for three population groups in British Columbia (BC): total population, Aboriginal off-reserve, and Aboriginal on-reserve populations. We also examined sociodemographic, geographic and ethnic risk markers for disparities. METHODS: We identified Aboriginal people through BC's universal health care insurance plan insurance premium group and birth and death record notations. We calculated crude incidence rate and Standardized Relative Risk (SRR) of hospitalization for unintentional transport injury, standardized for age, gender and Health Service Delivery Area (HSDA), relative to the total population of BC. We tested hypothesized associations of geographic, socio-economic, and employment-related characteristics of Aboriginal communities with SRR of transport injury by multivariable linear regression. RESULTS: During the period 1991-2010, the SRR for the off-reserve Aboriginal population was 1.77 (95% CI: 1.71 to 1.83); and 2.00 (95% CI: 1.93 to 2.07) among those living on-reserve. Decline in crude rate and SRRs was observed over this period among both the Aboriginal and total populations of BC, but was proportionally greater among the Aboriginal population. The best-fitting multivariable risk marker model was an excellent fit (R2 = 0.912, p<0.001), predicted SRRs very close to observed values, and retained the following terms: urban residence, population per room, proportion of the population with a high school certificate, proportion of the population employed; and multiplicative interactions of Aboriginal ethnicity with population per room and proportion of the population employed. CONCLUSIONS: Disparities in risk of hospitalization due to unintentional transport injury have narrowed. Aboriginal ethnicity modifies the effects of socioeconomic risk factors. Continued improvement of socioeconomic conditions and implementation of culturally relevant injury prevention interventions are needed.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| 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".