Protecting oneself while supporting the organisation: A longitudinal exploratory study of healthcare workers’ coping strategies and organisational resilience processes in the first year of the COVID-19 pandemic
Bibliographic record
Abstract
• A ‘problem-solving’ coping style was more frequent than positive thinking, seeking social support, and avoidance. • Coping strategies depended on the type of problematic situations experienced. • ‘Positive thinking’ and ‘problem solving’ coping styles were associated with work efficiency, team performance and learning. • Organisational resilience did not seem to come at the cost of individual health. • Coping strategies differed depending on professions, seniority and hierarchical status. The COVID-19 pandemic has been a major source of stress for health professionals and health institutions. In response, healthcare workers adapted their behaviours to protect their health and the organisational resilience of their institutions. The study aimed to explore these individual coping and organisational resilience strategies and their evolution during the first year of the pandemic. Based on a mixed and longitudinal protocol, the study included staff from several French-speaking Swiss healthcare institutions. Participants completed an online questionnaire three times during the first year of the pandemic. They described daily problematic work situations, coping styles, and organisational resilience strategies. ‘Problem solving’ was the most frequently reported coping style, followed by ‘positive thinking’, and in a lesser extent ‘seeking social support’ and ‘avoidance’. A high level of ‘problem solving’ and ‘positive thinking’ was associated with well-managed situations, learning and development of new work practices and higher team performance. A higher level of ‘seeking social support’ and ‘avoidance’ tended to be associated with high-risk problematic situations that hindered organisation resilience. Coping strategies differed depending on profession, job tenure and hierarchical status. The article concludes with recommendations for improving both organisational resilience and individual workers’ well-being in healthcare institutions.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".