Role of emotions in change and change management in an emergency department: a qualitative study
Bibliographic record
Abstract
BACKGROUND: Changes in emergency departments are frequently implemented to improve efficiency and reduce costs. However, staff acceptance and adoption are crucial for the intended success of changes. OBJECTIVES: This study explored staff perceptions of factors influencing the implementation of changes and any common themes linking changes and factors influencing changes in an emergency department at a university teaching hospital in the UK. METHODS: We used constructivist grounded theory methodology to perform a secondary analysis of 41 interview transcripts of physicians, nurses, support workers and managers involved in paediatric emergency care. RESULTS: Participants identified leadership, communication and education as factors impacting change management. They described many emotions associated with changes and with communication, leadership and education or the lack of any of them during changes. Both positive and negative emotions sometimes coexisted at individual, team or organisational levels. Negative emotions were due to real-life challenges and concern over compromised patient care. Professional values dictated the actions or inactions that transpired either because of these emotions or despite these emotions in health professionals. CONCLUSIONS: Emotions to change should be acknowledged and addressed by credible leadership clear communication and education to improve the change process, its success and ultimately, patient care.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".