(A165) Red Cross Health Erus, a Modular Approach to the Challenge of Evolving Emergencies
Bibliographic record
Abstract
Emergency Response Units (ERUs) were pioneered over a decade ago by the International Federation of Red Cross and Red Crescent Societies (IFRC), with the intention of providing a standardized, rapid global tool for response in disasters. Health ERUs are one example of several types of ERUs on stand-by in various countries around the world. Their tented infrastructure, basic medical equipment, and pre-trained personnel allow for the provision of surge medical capacity where it is needed. Commonly used set-ups include a Basic Health Care Unit and a Referral Hospital. The recently-introduced Rapid Deployment Emergency Hospital allows for a lighter, highly mobile infrastructure, with surgical and emergency medical capacity. The modular design of these ERUs allows for deployment with materials “tailored” to the disaster. Their flexibility has been demonstrated in public health emergencies such as the nation-wide cholera epidemic that occurred in Zimbabwe (2008) and more recently in earthquake-damaged Haiti (2010) and flood-affected Pakistan (2010). Health ERUs already on the ground in post-earthquake Haiti were able to re-organize equipment for use in cholera treatment units and centers, and additional ERUs were deployed specifically to set-up treatment centers. In Pakistan, a mobile clinic set-up was used to deliver primary health services to displaced populations, including psychosocial support initiatives and community health messages to minimize the emergence of communicable diseases. The Community Health module (CHM) is a new module in development since 2009. Experience has shown that disrupted health systems, combined with displaced populations can create a fertile environment for communicable disease outbreaks. The CHM addresses primary, secondary and tertiary prevention early in emergencies by engaging communities and more specifically National Society volunteers in epidemic control. The modular design of Health ERUs allow for a rapid and comprehensive approach to delivery of health care in a disaster, with a longitudinal perspective of population needs.
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.054 | 0.020 |
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".