Resilience of Health Systems: Understanding Uncertainty Uses, Intersecting Crises and Cross-level Interactions Comment on "Government Actions and Their Relation to Resilience in Healthcare During the COVID-19 Pandemic in New South Wales, Australia and Ontario, Canada"
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The coronavirus disease 2019 (COVID-19) pandemic has created opportunities to study resilience in multiple, interrelated societal systems while considering the institutional, community and individual level. We aim to discuss critical, yet underrepresented, issues in resilience discourses which are fundamental to advance theories, concepts and measurement of health system resilience. These relate to a better understanding of (i) how government’s handle and use uncertainties to facilitate or impede change, including the role of negotiation and conflicts, (ii) the intersections of health with multiple, co-occurring crises (systemic intersections), and (iii) cross-level interactions, ie, the interrelation between individual-level resilience, the collective resilience of groups and communities, and the resilience of a system as a whole (and vice versa). Analyses of these aspects can help to "contextualize" our understanding of resilience in complex adaptive systems. However, conceptual clarity is needed whether resilience is considered an underlying feature, outcome, or intermediate determinant of a (health) system’s performance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it