Leaders in emergency: an analysis of leadership processes of long-term care facilities leaders during the COVID-19 pandemic in Norway
Bibliographic record
Abstract
The aim of this study was to understand the leadership behaviours and processes emerging in healthcare leaders when facing a major crisis.In particular, the following research question guided the work: "how did long-term care facilities (LTCF) leaders manage the COVID-19 crisis?"Micro-level leaders working in nursing home facilities in Norway were chosen as the study subject, and their experiences in the first months of the COVID-19 pandemic as the time period of focus.LTCF facilities faced demanding challenges especially at the beginning of the pandemic, and leadership is especially important in disruptive situations, including at the micro-level.The COVID-19 pandemic was a unique event to be studied and rich of possible insights for future learning in the healthcare sector.14 leaders from Norwegian nursing homes participated in one-to-one interviews, which were coded and then analysed through framework analysis.Transformational leadership and sensemaking theories were used to analyse the findings.The focus was on ascertaining if transformational and sensemaking approaches emerged in the leaders in order to manage the COVID-19 crisis.Findings showed that leaders preferred transformational approaches, centred on personal influence, openness to communication and inputs, focus on the staff's individual needs and positive performance, rather than tradeoff-based approaches.Transformational leadership traits seemed to promote sensemaking approaches, aimed at building collective understanding and narratives around the new, unforeseen emergency.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".