(A306) Primary Care in the First 72 Hours Post Disaster: A Crazy Idea or a Sensible Inclusion for Foreign Medical Teams?
Bibliographic record
Abstract
The use and number of Foreign Field hospitals and Foreign Medical Teams being mobilized after sudden onset disasters in the past decade has increased significantly. Examples include Haiti (2010), China (2008) Pakistan (2005), and Iran (2003). Foreign medical teams do not just work in field hospitals anymore and new trends of how FMTs are engaged need to be taken into consideration. After sudden impact disasters, there is undoubtedly a high need for surgical response. The role of primary care, immediately after a disaster or emergency has sometimes been described as low priority and therefore not needed during the initial response to disasters and emergencies. This oral presentation will review trends in the primary care needs post disaster and the literature around it. Using the Health Resource Availability Mapping System (a model that is derived from the standard health cluster tool and used for collection, collation and analysis of health sector information) and modified to sudden onset disasters, which primary health services when will be reviewed. Discussion and brainstorming encouraged!
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.032 | 0.008 |
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".