Theorising Health System Resilience and the Role of Government Policy- Challenges and Future Directions Comment on "Government Actions and Their Relation to Resilience in Healthcare During the COVID-19 Pandemic in New South Wales, Australia and Ontario, Canada"
Bibliographic record
Abstract
Resilient healthcare (RHC) emphasises the importance of adaptive capacity to respond to unanticipated crises such as the global coronavirus disease 2019 (COVID-19) pandemic but there are few examples of RHC research focusing on the decisions taken by macro level policy makers. The Smaggus et al paper analyses the actions of two governments in Canada and Australia as described in media releases from a resilience perspective. The paper clearly articulates the need for conceptual clarity when analysing system resilience, and integrates three theoretical perspectives to understand the types of government responses and how they were related to resilience. The paper makes a valuable contribution to the developing RHC evidence base, but challenges remain in identifying conceptual frameworks, researching macro level resilience, including identifying and accessing reliable macro level data sources, analysing interactions between macro, meso and micro system levels, and understanding how resilience manifests at different temporal and spatial scales.
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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 | 0.017 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.015 | 0.025 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.041 | 0.031 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".