Pandemic Management in a Pediatric Hospital
Bibliographic record
Abstract
OBJECTIVES: To describe our experiences in the management of the second wave of influenza A H1N1 (pH1N1) pandemic in a tertiary-care children's hospital. METHODS: An autoethnographic study of the pandemic planning and management committee members involved in managing the second wave of pH1N1 was conducted. RESULTS: Staffing, surge capacity, communications and emergency operations planning by adding leaders of frontline workers and other key operational roles to the incident management team, and creating a tactical response team emerged as important factors in pandemic management in our hospital. The emergency department visits increased by 50%, necessitating increased staffing of the emergency department. Communications using existing chains of command had to be used to reach frontline staff during the pandemic. CONCLUSIONS: Incident management teams managing pandemics and other disasters have to be dynamic and create tactical teams to ensure implementation and facilitate bidirectional communication with frontline workers.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.011 |
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 teacher head, 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".