Organizational Resilience Indicators Based on a Salutogenic Orientation
Bibliographic record
Abstract
Disasters such as the recent parliament shooting in Ottawa, Superstorm Sandy, and the Great Japan Sea Earthquake and tsunami are reminders of the roles essential service organizations have in maintaining public health. On a daily basis, organizations are expected to operate under normal conditions, providing goods, services, and community supports. In crisis situations, it is critical that these organizations continue to operate and contribute to adaptive response and recovery in a community. Business continuity planning focuses on ensuring continued functioning of core operations during a disruption. Inherent to the business continuity field is a prevent-and-protect approach to preparedness activities. Asset-mapping exercises have the potential to balance the predominantly risk-based field by focusing on the strengths and capabilities already present within an organization. To understand the value of asset-mapping activities in business continuity plans (BCPs), indicators for organizational resilience are needed. Indicators have the potential to provide essential service organizations with a way to gauge the value of their BCP activities. In addition, this information can help guide decision-makers when developing BCPs. This research is part of a larger project at the University of Ottawa focused on building the empirical evidence base for BCPs and organizational resilience. This thesis, as a sub-study within the larger project, explores assets and indicators for organizational resilience to contribute to the effective evaluation and engagement of organizations in business continuity planning efforts. Emergent themes highlight the importance of assets and their contribution to the adaptive capacity of an organization in the event of a disaster. This study also provides an example list of 28 SMARTT organizational resilience indicators directly derived from organizational assets, providing information that researchers and essential service organizations can use to evaluate business continuity planning activities in relation to organizational resilience.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".