Management behaviors during the COVID-19 pandemic: The case of healthcare middle managers
Bibliographic record
Abstract
Background: The spread of COVID-19 has disrupted the lifestyles of the world's population. In the workplace, the pandemic has affected all sectors and has changed the way work is organized and carried out. The health sector has been severely impacted by the pandemic and has faced enormous challenges in maintaining healthcare services while providing care to those infected by the virus. At the heart of this battle, healthcare managers were key players in ensuring the orchestration of operations and the physical and mental availability of employees during the crisis. Although few studies have been conducted to identify organizational practices or leadership skills to be adopted in a crisis context, the concrete behaviors of managers have not been documented yet. Therefore, this study aims at filling this gap by studying middle managers' behaviors facing COVID-19 crisis in the healthcare sector. Methods: Using a qualitative approach, eight focus groups were conducted online during the pandemic with 37 middle managers from the healthcare community of a Quebec health establishment (Canada) from April to June 2020. Thematic analyses were conducted, and a mixed-methods approach was used to analyse the data based on Viitala's hierarchical model of management skills. Results: Based on the six managerial skills proposed in the model of Viitala, 21 specific management behaviors were identified as having been deployed by middle managers at the beginning of the pandemic. Considering that the health sector has been profoundly shaken by this health crisis, in addition to being an environment likely to experience other crises, managers need to develop practical skills in various crisis management situations. Thus, the results guide practitioners by highlighting the importance of team-oriented management behaviors (leadership, supervisory competencies), especially in a crisis context.
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.007 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".