How resilience affected public health research during COVID-19 and why we should abandon it
Bibliographic record
Abstract
Resilience has accompanied the COVID-19 pandemic as a rallying motto, with calls by governments for a resilient society, resilient families and schools, and, of course, resilient healthcare systems in the face of this unprecedented pandemic shock. Resilience had already gained traction as an analytical concept in public health research for approximately a decade. It became a key concept despite the recognition of its lack of conceptual consistency. The COVID-19 pandemic presented itself as a perfect test-case and encouraged a multiplicity of studies on resilience and health care systems. In this commentary, we add to the existing critiques of resilience in the social sciences by reflecting on the effects of resilience when used to frame empirical inquiries and to draw lessons from the crisis. Resilience as a concept is unable to address crucial structural issues that health systems already faced throughout the world, and it remains a non-neutral political notion. We argue that we need to resist a generalised view of resilience and work with alternative imaginaries.
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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 | 0.155 | 0.188 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.019 | 0.110 |
| Scholarly communication | 0.025 | 0.044 |
| Open science | 0.005 | 0.020 |
| Research integrity | 0.028 | 0.049 |
| Insufficient payload (model declined to judge) | 0.002 | 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".