Bibliographic record
Abstract
Many countries around the world have what is known as a Good Samaritan Law, protecting the rights of those citizens that go out of their way to help those in need, most commonly in emergency situations. This protection may well be a step in the right direction, but does not address the fundamental issue of first aid knowledge. If more people were trained in this life-saving skill, businesses and the general public would have greater confidence in knowing that valuable help was at hand. Up to 150 000 people die each year in situations where first aid may have made the difference – four times the number who die each year from the biggest cause of cancer or the equivalent of the entire population of Basingstoke. Our research found that almost two thirds of the general public wouldnt feel confident in administering first aid in an emergency. Even more disturbingly, a quarter would stand by and do nothing, hoping that someone else knows first aid. In spite of these alarming statistics, the UK like many other countries is still guilty of treating first aid as a legislative requirement, rather than a necessary life skill. In communities where medical services might not be easily available, a little first aid knowledge could go a long way. Richard Evens, commercial training director, St John Ambulance, will discuss the importance of first aid knowledge for citizens of all ages, giving examples of where these skills have made a difference between life and death.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.005 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.058 | 0.021 |
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".