Bibliographic record
Abstract
Medical staff need to be aware of major incident planningEditor-Last month the BMA warned that too few people in the United Kingdom know how to respond to a terrorist attack. 1 Its concerns about too few senior officials being aware of plans and recommendations to improve preparedness are sensible, but we believe that not enough medical staff are aware of their role in the event of a terrorist attack.We recently carried out a survey in the largest acute NHS trust of the south west of England to assess medical staff's knowledge about the local major incident plan.We sent questionnaires to the 107 doctors in North Bristol NHS Trust with a potential role in the mobile medical team if they were on duty during a major incident.Seventy seven doctors replied (72%).Sixty nine were aware of the existence of the local major incident plan, but only 26 had read part or all of it.Only 11 of the responding doctors were aware of their potential role in the mobile medical team.Of these 11 doctors, only three thought themselves adequately trained for this position, and all three had been trained as medical incident officers.Last year's National Audit Office report highlighted deficiencies in NHS plans to deal with major incidents in England. 2 It recommended upgrading training arrangements.Five months later some doctors are still unaware of their roles in the event of a terrorist attack.As a trust we are currently considering several measures to improve on our results.We suspect, however, that our findings are not unique and encourage other acute trusts to look closely at their staff's knowledge and training and act accordingly.
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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 | 0.006 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.022 | 0.012 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.494 | 0.323 |
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".