Gaining and maintaining commitment to large-scale change in healthcare organizations
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Healthcare administrators have sought to improve the quality of healthcare services by using organizational change as a lever. Unfortunately, evaluations of organizational change efforts in areas such as total quality management (TQM), continuous quality improvement (CQI), and organizational restructuring have indicated that these change programmes have not fulfilled their promise in improving service delivery. Furthermore, there are no easy answers as to why so many large-scale change programmes are unsuccessful. The aim of this analysis is to provide insights into practices that may be utilized to improve the chances of successful change management. It is proposed that in order to effect change, implementers must first gain commitment to the change. This is done by ensuring organizational readiness for change, surfacing dissatisfaction with the present state, communicating a clear vision of the proposed change, promoting participation in the change effort, and developing a clear and consistent communication plan. However gaining commitment is not enough. Many change programmes have been initially perceived as being successful but long-term success has been elusive. Therefore, maintaining commitment during the uncertainty associated with the transition period is imperative. This can be done by successfully managing the transition using action steps such as consolidating change using feedback mechanisms and making the change a permanent part of the organization's culture.
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.
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 | 0.017 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 it