Examining Injury Among Indigenous Young People: A Narrative Scoping Review
Bibliographic record
Abstract
Indigenous young people of circumpolar developed countries experience a disproportionate incidence of injury compared with non-Indigenous young people. Much has been published about the experience of injury within the general Indigenous population, but literature is limited with regard to Indigenous young people. Trauma nurses, who are important members of the multidisciplinary team that provides injury intervention to Indigenous populations, need to be aware of this literature, as well as subsequent research gaps, to provide effective care to Indigenous young people experiencing injury. The purpose of this study was to provide an updated comprehensive review of the research-based evidence related to all-cause injury for Indigenous young people. An exploratory scoping review was conducted with a narrative synthesis. To locate literature, 7 electronic databases were searched from 2003 to 2017. Criteria were applied to all located articles to determine inclusion and then data were extracted from each study's findings. A total of 15 studies were included in this review that met the inclusion criteria. Themes derived from the knowledge synthesis process are as follows: incidence rates, mechanisms of injury, risk factors, and gaps in research. A strong epidemiological focus has emerged from the research found in this review of injury among Indigenous young people. The results of this review serve as a starting point for trauma nurses to successfully assess, build trusting relationships with, and further research injury among Indigenous young people.
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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 | 0.011 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".