How things changed during the COVID-19 pandemic’s first year: A longitudinal, mixed-methods study of organisational resilience processes among healthcare workers
Bibliographic record
Abstract
COVID-19 had a huge impact on healthcare systems globally. Institutions, care teams and individuals made considerable efforts to adapt their practices. The present longitudinal, mixed-methods study examined a large sample of healthcare institution employees in Switzerland. Organisational resilience processes were assessed by identifying problematic real-world situations and evaluating how they were managed during three phases of the pandemic’s first year. Results highlighted differences between resilience processes across the different types of problematic situations encountered by healthcare workers. Four configurations of organisational resilience were identified depending on teams’ performance and ability to adapt over time: “learning from mistakes”, “effective development”, “new standards” and “hindered resilience”. Resilience trajectories differed depending on professional categories, hierarchical status and the problematic situation’s perceived severity. Factors promoting or impairing organisational resilience are discussed. Findings highlighted the importance of individuals’, teams’ and institutions’ meso- and micro-level adaptations and macro-level actors’ structural actions.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".