Enhancing Learning, Innovation, Adaptation, and Sustainability in 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 development of sustainable health care organizations that provide high-quality accessible care is a topic of intense interest. This article provides a practical performance management framework that can be utilized to develop sustainable health care organizations. It is a cyclical 5-step process that is premised on accountability, performance management, and learning practices that are the foundation for a continuous process of measurement, disconfirmation, contextualization, implementation, and routinization This results in the enhancement of learning, innovation, adaptation, and sustainability (ELIAS). Important considerations such as recognizing that health care organizations are complex adaptive systems and the presence of a dynamic learning culture are necessary contextual factors that maximize the effectiveness of the proposed framework. Importantly, the ELIAS framework utilizes data that are already being collected by health care organizations for accountability, improvement, evaluation, and strategic purposes. Therefore, the benefit of the framework, when used as outlined, would be to enhance the chances of health care organizations achieving the goals of ongoing adaptation and sustainability, by design, rather than by chance.
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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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