Transformation or change: some prescriptions for health care organizations
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.
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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 | 0.022 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.013 | 0.094 |
| Scholarly communication | 0.021 | 0.029 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.023 | 0.028 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".