Organizational change readiness and manager' behavior in managing change
Bibliographic record
Abstract
Background and objective: Organizational readiness refers to organizational members’ change commitment and change efficacy to implement organizational change and confidence in their accumulative abilities to do so. The aim of the study was to assess the nurse managers’ behavior in managing change, and the level of the organizational change readiness at selected hospitals at Menofia Governorate.Methods: Design: A descriptive cross-sectional design was used. Setting: The study was conducted at two hospitals, namely University Hospital and Shebin El-Kom Teaching Hospital, in Menofia Governorate, Egypt. Subjects: It consisted of two groups, Group 1: A convenience sample of 136 staff nurses (67 staff nurses from Menofia University Hospital, and 69 from Shebin El-Kom Teaching Hospital), Group 2: All nurse manager available on the time of the study (31 from Menofia University Hospitals, and 30 from Shebin El-Kom Teaching Hospital). Tools: a) Tool one: Change management process Questionnaire, b) Tool two: Change Readiness Assessment Scale.Results: More than half of the nurse managers reported that they have a good behavior in managing change in the organization, while the staff nurses reported that their managers had a bad behavior during the change process. Organizational readiness level was higher in University hospitals than in Teaching hospital as perceived by the study subjects.Conclusions: The nurse managers and staff nurses reported that the organization had a bad readiness level to change. Additionally there was a positive correlation between organizational readiness and manager behavior in managing change. Recommendations: Organization should have a readiness for change to support the change process by possessing the right resources and conditions, a clear insights and goals for the intended change and have the inventiveness, behavior to participate with the change and develop work. Also, agents of change chiefs and management must need to drive a strong reaction for change from the stakeholders that leads to highest performance improvement.
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 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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".