Resilient health care performance in the real world: fixing problems that never happened
Bibliographic record
Abstract
BACKGROUND: Staff in health systems everywhere have exhibited flexibility and a capacity for improvisations during, and in response to, the COVID-19 pandemic. Looking to other examples of such resilient behaviours outside of those induced by the pandemic is instructive for those involved with researching or understanding change, or making health systems improvements. METHODS: Here, we synthesise and then assess the value of eight case studies of in situ resilient performance from Canada, Sweden, Japan, Belgium, the United Kingdom, Norway, the United States and Brazil. The cases are divided into four categories: responsiveness to a crisis; adaptiveness over time; local adoption in accommodating to a top down, national policy change; and the consequential outcomes of an intervention. RESULTS: The cases illuminate the resourcefulness of translational and social researchers in examining such behaviours and practices. More than that, they also foreground the ingenuity and adaptive capacity of staff on-the-ground who continually anticipate, respond and adapt to make systems work and provide continuous care in the face of many challenges, including resource deficiencies, policy misalignments, and new technologies, policies and procedures that need to be integrated into local workflows. Front line clinicians make care systems work, pre-empting issues and sorting out problems before they occur or as they arise. CONCLUSIONS: A key lesson amongst a range of findings is that, rather than focusing on shiny new tools of change (checklists, frameworks, policy mandates), it is much more insightful and satisfying to deeply apprehend care at the sharp end, where clinicians deliver care to patients, understanding how everyday work is executed. This, rather than the Health Ministry, the Boardroom, or the Management Consultant's office, is where and how change is being enabled, and where street level actors solve problems, thwart issues in advance, and constantly avoid pitfalls.
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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 | 0.052 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.017 | 0.077 |
| Scholarly communication | 0.018 | 0.026 |
| Open science | 0.005 | 0.022 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".