Bibliographic record
Abstract
Debra reid/ap/empics Is the shell suit making a comeback? Medical students haven't traditionally been trained to respond in disaster situations. Now a university in Canada is trying to establish an ambitious training programme that will cater for their training needs. Amy Cheng finds out if medical students are entering a new era of emergency response The London tube bombings—52 dead. The Asian tsunami—220000 dead. The 11 September attacks—2986 dead. Hurricane Katrina—1163 dead. As these figures and images of devastation flashed across newspaper front pages and television screens, I wondered, as a senior medical student, what I could do to help? I was travelling in South East Asia when the tsunami struck. As soon as I heard about it and the devastation it caused, I immediately submitted an application to volunteer for the Doctors Without Borders/Medecins Sans Frontieres' emergency relief projects in the affected regions. My enthusiasm was quickly met by disappointment when my application was rejected. According to the organisation, medical students are rarely accepted because we are more of a liability than an asset. Our insufficient medical training, in addition to our need for supervision, diverts the organisation's resources away from the people we are trying to help. In fact, the literature shows that even experienced medical staff can become a burden, rather than a help at the scene of a disaster. Without sufficient training in emergency response and disaster relief, healthcare workers can become victims themselves, without changing the morbidity and mortality of the population they are trying to help.12 Ever since the attacks on September 11, there has been an explosion of papers that draw our attention to the scarce number of physicians who …
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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; both teacher heads agree on what is shown here.
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".