First-line managers’ perspectives of employee engagement, burnout, and active disengagement during the COVID-19 pandemic
Bibliographic record
Abstract
The COVID-19 global pandemic created unprecedented rapid change and increased organizational stress, especially in healthcare organizations. Employee engagement rates decrease during times of change and organizational stress, such as the pandemic. Positive patient outcomes, including lower mortality rates, are attributed to engagement. Germane to this study is that first-line managers are key to maintaining and improving employee engagement. The concept of employee engagement is consistent; however, the concepts that are opposite to engagement including burnout and active disengagement differ. The question central to this study is, if different concepts are described as opposite to employee engagement, how are these concepts identified, understood, and subsequently addressed through management interventions, and do the phenomena require different interventions? If first-line nurse managers are expected to positively impact employee engagement and minimize burnout and active disengagement post-pandemic, further exploration of how this triad of concepts is understood, perceived, and experienced by first-line nurse managers is essential. The study occurred in a Western Canadian province impacted by the global pandemic. Using interpretive descriptive methodology, interviews were conducted with first-line healthcare managers to gain an understanding of participants’ perspectives of the phenomena integral to the study. This study is significant because there is a dearth of research regarding the triad of phenomena, active disengagement, burnout, and engagement, despite the influence of the outcomes of the concepts on staff, patients, and organizations. Understanding what engagement is not, and how first-line managers can address staff engagement is paramount to recruiting and retaining healthcare workers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".