Experiences of Frontline Managers during the COVID-19 Pandemic: Recommendations for Organizational Resilience
Bibliographic record
Abstract
The COVID-19 pandemic caused a global health crisis directly impacting the healthcare system. Healthcare leaders influence and shape the ability of an organization to cope with and recover from a crisis such as the COVID-19 pandemic. Their actions serve to guide and support nurses' actions through unpredictable health service demands. The purpose of this paper was to examine frontline managers' experiences and organizational leadership responses that activated organizational resilience during the COVID-19 pandemic, and to learn for ongoing and future responses to healthcare crises. Fourteen managers participated in semi-structured interviews. We found that: (1) leadership challenges (physical resources and emotional burden), (2) the influence of senior leader decision-making on managers (constant change, shortage of human resources, adapting care delivery, and cooperation and collaboration), and (3) lessons learned (managerial caring behaviours and role modelling, adaptive leadership, education and training, culture of care for self, and others) were evidence of managers' responses to the crisis. Overall, the study provides evidence of managers experiences during the early waves of the pandemic in supporting nurses and fostering organizational resilience. Knowing manager's experiences can facilitate planning, preparing, and strengthening their leadership strategies to improve work conditions is a high priority to manage and sustain nurses' mental health and wellbeing.
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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 | 0.012 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".