Preparation for the next major incident: are we ready? A 12-year update
Bibliographic record
Abstract
OBJECTIVES: A major incident is any emergency that requires special arrangements by the emergency services and generally involves a large number of people. Recent such events in England have included the Manchester Arena bombing and the Grenfell Tower disaster. Hospitals are required by law to keep a major incident plan (MIP) outlining the response to such an event. In a survey conducted in 2006 we found a substantial knowledge gap among key individuals that would be expected to respond to the enactment of an MIP. We set out to repeat this survey study and assess any improvement since our original report. METHODS: We identified NHS trusts in England that received more than 30 000 patients through the emergency department in the fourth quarter of the 2016/2017 period. We contacted the on-call anaesthetic, emergency, general surgery, and trauma and orthopaedic registrar at each location and asked each individual to answer a short verbal survey assessing their confidence in using their hospital's MIP. RESULTS: Of those eligible for the study, 62% were able to be contacted and consented to the study. In total 50% of respondents had read all or part of their hospital's MIP, 46.8% were confident that they knew where their plan was stored, and 36% knew the role they would play if a plan came into effect. These results show less confidence among middle-grade doctors compared with 2006. CONCLUSIONS: Confidence in using MIPs among specialty registrars in England is still low. In light of this, we make a number of recommendations designed to improve the education of hospital doctors in reacting to major incidents.
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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 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.012 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.017 | 0.009 |
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".