Injury documentation: Using the BALD STEP Mnemonic and the RCMP Sexual Assault Kit
Bibliographic record
Abstract
Injury assessment, nomenclature and documentation vary widely across emergency departments. The Royal Canadian Mounted Police (RCMP) has forensic laboratories across Canada and performs the majority of testing of evidence for Canadian victims of sexual assault outside of Ontario or Quebec. Most emergency nurses are familiar with the large box the RCMP officers bring for the documentation and collection of evidence after sexual assault. Fewer are familiar with the inner contents of the kit or documentation. The sexual assault kit and documentation are now undergoing change, with a new kit soon to be released. The RCMP forensic laboratories identified the need for kit changes due to equipment updates and research related to quality and best practices. Some health care professionals across the country and representatives of Making a Difference Canada were consulted for suggestions on documentation and kit contents. Mount Royal University’s Forensic Research Network (www.mtroyal.ca/forensicresearch) will be developing free educational materials on the kits and use of contents in preparation for the release of the new kits (hoped to be late 2011, but not yet determined). In the meantime, the focus of this article is to introduce emergency nurses to a few key changes in documentation and evidence collection they may anticipate. The largest of these changes is the adoption of a mnemonic phrase to aid in standardization of injury documentation and assessment known as BALD STEP.
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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 | 0.008 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
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".