Perfect Storm: Organizational Management of Patient Care Under Natural Disaster Conditions
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
Managing uncertainty is an essential attribute of organizational leadership and effectiveness. Uncertainty threatens optimal decision making by managers and, by extension, reduces the quality of patient care. Variation in the work flows of everyday patient caregiving reflects management's steps to control uncertainty, which include strategies for contending with potential disaster scenarios. Little exists in the literature that reveals how management's strategic response to controlling uncertainty in a real disaster event differs from strategies practiced in disaster simulations, with the goal of protecting patient care. Using organization theory, this article presents the application of uncertainty management to the catastrophic flooding of a major teaching hospital. A detailed description of management's strategies for patient rescue and evacuation is provided. Unique aspects of managing uncertainty stemming from a natural disaster are highlighted. Recommendations on organization responses to disasters that optimize patient care, safety, and continuity are offered to managers.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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