Emergency health surge support: Lessons learned from a review of Red Cross responses, 2015-2019
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The nature of humanitarian response has evolved in response to increasing humanitarian needs, number and scale of emergencies, and the expansion of certified Emergency Medical Teams. This research examines the International Federation of Red Cross and Red Crescent Societies' clinical and public health Emergency Response Units in emergencies from 2015 through 2019 using a mixed methods approach, consisting of a desk review and primary qualitative data, to inform prioritization of response activities and optimization of health surge support in emergencies. Identified opportunities for improvement include needs assessment, increased modularity, context-appropriate support/integration, human resources and capacity building, monitoring and evaluation, and the overall nature of health surge response to various emergency types. Greater focus on public health response; standardizing deployment criteria, standard operating procedures, and monitoring for clinical surge support; and regional and local capacity building could all improve health service quality and sustainability and facilitate more cost-effective emergency response.
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.
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 | 0.014 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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 it