Identifying Leading Indicators of System Resilience and the Strategy for Developing Them: A Review of Reviews
Bibliographic record
Abstract
Abstract Background: System resilience describes the performance or capacity of a system to absorb and adapt in response to stressors and perturbations. This protocol outlines the search strategy to identify indicators or metrics of system resilience from across diverse fields and examine the methods/processes for developing these indicators or metrics.Methods: This protocol is grounded in the Joanna Briggs Institute (JBI) Scoping Review methodology. We will search Web of Science and Scopus for reviews that report on system resilience assessment and measurement techniques. We will also employ snowball sampling techniques. Due to the breadth of the topic, this search will be limited to systematic reviews, meta-analyses, or literature reviews with a structured, documented search strategy reporting on primary research studies published in the English language. We will not limit the search by date or discipline. Reviews that report on psychological resilience or resilience at the level of an individual will be excluded. Discussion: The ability to assess or measure resilience is important to identify risks, opportunities, and strategies to influence sustained, reliable, and predictable system operations. We hypothesise that there are commonalities in the types of indicators used and how they are developed. These patterns may have relevance to other fields, like healthcare. The findings from this review may help others develop their own indicators and assessment strategies for understanding resilience within their own systems. Ethics and Dissemination: This study is ethics exempt. We will disseminate the results of this work at national and international quality and safety conferences, and through publication of a manuscript.
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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 | 0.049 | 0.207 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.007 | 0.011 |
| Bibliometrics | 0.052 | 0.042 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.013 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".