Transformation or change: some prescriptions for health care 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
The powerful forces that are transforming healthcare can generate enormous economic potential for those who are able to employ effective survival techniques in the short term and at the same time plan for success in the long term. To accomplish this, an organization must harness the forces driving transformation and use them to its advantage. Despite the best efforts of senior healthcare executives, major change initiatives often fail. Change threatens the very stability and continuity that managers are attempting to control; therefore change and managers are not natural partners. Even managers aware of the need to change resist the parts that appear too major, too risky, or too “different”. This understanding of change, transformation and reinvention are crucial for all health‐care organizations moving forward at turbulent speeds. Change has its problems and successes are not abundant. This article will examine change strategies; their failures and successes; the role of the leader in this process; overcoming barriers and resistance, key steps to succeed in change efforts and, finally, alternative strategies to build the change process.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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