Epidemiology and healthcare utilization of First Nations peoples living with spinal cord injury in Alberta: an observational study to explore health inequities
Bibliographic record
Abstract
STUDY DESIGN: Retrospective observational cohort study. OBJECTIVES: Estimate spinal cord injury (SCI) prevalence in First Nations and non-First Nations populations and compare healthcare utilization as an indirect marker of health inequities. SETTING: Alberta, Canada. METHODS: We created a prevalent adult SCI cohort by identifying cases between April 1, 2002 and December 31, 2017 who were followed for common SCI complications and location of healthcare access from January 1, 2018 to December 31, 2019 using administrative data sources housed within Alberta Health Services (AHS). First Nations and non-First Nations SCI cohorts were divided into SCI etiology: traumatic SCI (TSCI) and non-traumatic SCI (NTSCI). Statistical analyses compared prevalence, demographics, healthcare utilization, and SCI complication rates. A secondary analysis was performed using case matching for demographics, injury type, injury level, and comorbidities. RESULTS: TSCI prevalence: 248 and 117 per 100,000 in First Nations and non-First Nations cohorts, respectively. NTSCI prevalence: 74 and 50 per 100,000 in First Nations and non-First Nations cohorts, respectively. Visit rates were higher in the TSCI First Nations cohort for visits to General Practitioner (GP), Emergency Department (ED), inpatient visits, and inpatient days with higher complication rates due to pulmonary, genitourinary, skin, and 'other' causes after case matching. Visits rates were higher in the NTSCI First Nations cohort for GP and specialists without differences in complication types after case matching. CONCLUSIONS: Significant differences exist between First Nations and non-First Nations cohorts living with SCI in Alberta, suggesting healthcare inequities against First Nations Peoples in this province.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 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) | 0.001 | 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".