Decision-making in crisis: Applying a healthcare triage methodology to business continuity management
Bibliographic record
Abstract
The concept of triage in healthcare has been around for centuries and continues to be applied today so that scarce resources are allocated according to need. A business impact analysis (BIA) is a form of triage in that it identifies which processes are most critical, which to address first and how to allocate limited resources. On its own, however, the BIA provides only a roadmap of the impacts and interdependencies of an event. When disaster strikes, organisational decision-makers often face difficult decisions with regard to allocating limited resources between multiple 'mission-critical' functions. Applying the concept of triage to business continuity provides those decision-makers navigating a rapidly evolving and unpredictable event with a path that protects the fundamental priorities of the organisation. A business triage methodology aids decision-makers in times of crisis by providing a simplified framework for decision-making based on objective, evidence-based criteria, which is universally accepted and understood. When disaster strikes, the survival of the organisation depends on critical decision-making and quick actions to stabilise the incident. This paper argues that organisations need to supplement BIA processes with a decision-making triage methodology that can be quickly applied during the chaos of an actual event.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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".