Adapting to the unexpected: Problematic work situations and resilience strategies in healthcare institutions during the COVID-19 pandemic’s first wave
Bibliographic record
Abstract
The COVID-19 pandemic's first wave required considerable adaptation efforts on the part of healthcare workers. The literature on resilient healthcare describes how the collective regulation strategies implemented by frontline employees make essential contributions to institutions' abilities to cope with major crises. The present mixed-methodology study was thus conducted among a large sample of employees in a variety of Swiss healthcare institutions and focused on problematic real-world situations experienced by them and their managers during the pandemic's first wave. It highlighted the anticipatory and adaptive strategies implemented by institutions, teams and individuals. The most frequently cited problematic situations involved organisational changes, interpersonal conflicts and workloads. In addition to the numerous top-down measures implemented by institutions, respondents also identified personal or team regulation strategies such as increasing staff flexibility, prioritising tasks, interprofessional collaboration, peer support or creating new communication channels to families. The present findings underlined the importance of taking greater account of healthcare support staff and strengthening managerial capacity to support interprofessional teams including those support staff.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".