750 Burn Injury in Our Region’s Indigenous Population
Bibliographic record
Abstract
Abstract Introduction Our Indigenous population is disproportionately affected by injury resulting in significant morbidity and mortality. Burn injuries in this population have not yet been explored. Barriers to healthcare faced by Indigenous people differ from non-Indigenous people and understanding these differences is essential to providing culturally safe care. Our research seeks to understand the characteristics of burns in our Indigenous population and the personal experiences of Indigenous burn survivors. Our aim is to raise awareness about the specialized needs of this population and provide cultural understanding to inform in-hospital care and repatriation to home communities. Methods Data was collected from our regional burn unit to examine burn characteristics between Indigenous and non-Indigenous burn survivors. The combined adult and pediatric burn registries were examined. Between 2008–2018 there were 615 complete observation data sets. Observations were grouped by Indigenous status, age, and urban/rural. Summary tables were constructed and t-tests performed to examine differences in burn severity and length of stay between the groups of interest. Results Indigenous burn patients in our region are younger at the time of injury and while they have a similar TBSA, their length of stay is considerably longer. Conclusions Burn injuries in the Indigenous population account for 25% of all admissions. Despite a similar burn size their injuries result in significantly longer stays in hospital. This may be because Indigenous burn patients are more likely to live in rural/remote settings far from specialized burn care compared to non-Indigenous patients. The distance from definitive care may the reason for the longer length of stay. Being far from their home community while in hospital is a unique challenge in this population. Future plans are to gain a better understanding of Indigenous burn patients and their barriers to care by completing a qualitative narrative analysis on Indigenous burn survivor healthcare experiences. This information will inform burn patient care in hospital and repatriation to home communities. Applicability of Research to Practice Burns are prevalent in Indigenous populations and understanding their experiences supports culturally competent care. The distance from definitive care seems to increase length of stay independent of the size of burn. This research aims to better understand this population so that we may better serve Indigenous burn patients.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".