A geospatial analysis of the relationship between neighbourhood socioeconomic status and adult severe injury in Greater Vancouver
Bibliographic record
Abstract
BACKGROUND: Every year, injuries cost the Canadian healthcare system billions of dollars and result in thousands of emergency room visits, hospitalisations and deaths. The purpose of this study was to explore the relationship between neighbourhood socioeconomic status (NSES) and the rates of all-cause, unintentional and intentional severe injury in Greater Vancouver adults. A second objective was to determine whether the identified associations were spatially consistent or non-stationary. METHODS: Severe injury cases occurring between 2001 and 2006 were identified using the British Columbia's Coroner's Service records and the British Columbia Trauma Registry, and mapped by census dissemination areas using a geographical information system. Descriptive statistics and exploratory spatial data analysis methods were used to gain a better understanding of the data sets and to explore the relationship between the rates of severe injury and two measures of NSES (social and material deprivation). Ordinary least squares and geographically weighted regression were used to model these relationships at the global and local levels. RESULTS: Inverse relationships were identified between both measures of NSES and the rates of severe injury with the strongest associations located in Greater Vancouver's most socioeconomically deprived neighbourhoods. Social deprivation was found to have a slightly stronger relationship with the rates of severe injury than material deprivation. CONCLUSIONS: Results of this study suggest that policies and programmes aimed at reducing the burden of severe injury in Greater Vancouver should take into account social and material deprivation, and should target the most socioeconomically deprived neighbourhoods in Greater Vancouver.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".