Burn mass casualty incident planning in Alberta: A case study
Bibliographic record
Abstract
Burn mass casualty incident (BMCI) preparedness is lacking across Canada. A focused exploration of the current policies, protocols and practices in Alberta that address the response to a BMCI was conducted. In this case study, data were gathered from documents outlining the health system response to a mass casualty incident and health care professionals directly involved. Interviews were conducted online, recorded and transcribed. Qualitative description was used to code common themes across documents and transcripts. Fifteen documents and nine participant interviews were included in this study. Overall, the current policies, protocols and practices in place were limited to all-hazards mass casualty incident planning and did not address the specialized needs of burn patients. Deficiencies included no burn-specific plan at each of the two burn centres, a lack of provincial-level recognition of the unique challenges associated with a BMCI and no established Canadian burn disaster communication plan. Suggestions of strategies for a burn plan included forward triage, patient movement, use of telemedicine, partnering skilled and non-skilled staff, and procuring additional supplies. For best patient outcomes the provincial health authority needs to provide dedicated time for burn care experts to develop BMCI response plans to better address this unique hazard.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".