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 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 | 0.027 | 0.036 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".