Hospital preparedness for Ebola virus disease: a training course in the Philippines.
Bibliographic record
Abstract
OBJECTIVE: To develop, teach and evaluate a training workshop that could rapidly prepare large numbers of health professionals working in hospitals in the Philippines to detect and safely manage Ebola virus disease (EVD). The strategy was to train teams (each usually with five members) of key health professionals from public, private and local government hospitals across the Philippines who could then guide Ebola preparedness in their hospitals. METHODS: The workshop was developed collaboratively by the Philippine Department of Health and the country office of the World Health Organization. It was evaluated using a pre- and post-workshop test and two evaluation forms. χ(2) tests and linear regression analyses were conducted comparing pre- and post-workshop test results. RESULTS: A three-day workshop was developed and used to train 364 doctors, nurses and medical technologists from 78 hospitals across the Philippines in three initial batches. Knowledge about EVD increased significantly (P < 0.009) although knowledge on transmission remained suboptimal. Confidence in managing EVD increased significantly (P = 0.018) with 96% of participants feeling more prepared to safely manage EVD cases. DISCUSSION: The three-day workshop to prepare hospital staff for EVD was effective at increasing the level of knowledge about EVD and the level of confidence in managing EVD safely. This workshop could be adapted for use as baseline training in EVD in other developing countries to prepare large numbers of hospital staff to rapidly detect, isolate and safely manage EVD cases.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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 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".