Promoting Resilience Using an Asset-Based Approach to Business Continuity Planning
Bibliographic record
Abstract
Essential service organizations fulfill critical roles in maintaining public health during a disaster; therefore, business continuity planning is paramount to ensure continued functioning of core operations during a disruption. Business continuity planning is typically oriented around a predict and prevent approach. Asset-mapping activities have the potential to balance the predominantly risk-based approach by focusing on strengths and capability already present within organizations. The purpose of this study is to identify a suite of organizational-level assets that support resilience, and to contribute to the empirical evidence base for business continuity planning. Two focus group consultations with essential service organization representatives ( n = 22) were held in Ottawa, Canada, in March and April 2014, using the Structured Interview Matrix facilitation format. Inductive analysis was used to identify eight emergent themes that highlight the importance of organizational-level assets and their contribution to adaptive capacity. Leadership and culture in adopting and promoting preparedness strategies were predominant themes, as well as the importance of communication and connectedness across micro, meso, and macro levels. A suite of 25 assets were identified and grouped into seven categories: (a) awareness, (b) human resources, (c) information and communication, (d) leadership and culture, (e) operational infrastructure, (f) physical resources, and (g) social capital. This evidence base can be used as a template to guide asset-mapping activities, and support organizations engaging in business continuity planning.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".